diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml
index 33d751ed1..420fe513f 100644
--- a/.github/workflows/checks.yml
+++ b/.github/workflows/checks.yml
@@ -50,9 +50,20 @@ jobs:
- "3.9"
- "3.10"
steps:
+ - name: Free Disk Space
+ run: |
+ sudo rm -rf /usr/share/dotnet
+ sudo rm -rf /usr/local/lib/android
+ sudo rm -rf /opt/ghc
+ sudo rm -rf /opt/hostedtoolcache/CodeQL
+ sudo docker image prune --all --force
+ df -h
- uses: actions/checkout@v3
- name: Install Poetry
uses: snok/install-poetry@v1
+ with:
+ virtualenvs-create: true
+ virtualenvs-in-project: true
- name: Set up Python
uses: actions/setup-python@v4
with:
@@ -69,7 +80,7 @@ jobs:
- name: Install dependencies
run: |
poetry check --lock
- poetry install --with dev
+ poetry install --sync --with dev
- name: Authenticate HuggingFace CLI
if: env.HF_TOKEN != ''
run: |
@@ -90,9 +101,20 @@ jobs:
name: Code Checks
runs-on: ubuntu-latest
steps:
+ - name: Free Disk Space
+ run: |
+ sudo rm -rf /usr/share/dotnet
+ sudo rm -rf /usr/local/lib/android
+ sudo rm -rf /opt/ghc
+ sudo rm -rf /opt/hostedtoolcache/CodeQL
+ sudo docker image prune --all --force
+ df -h
- uses: actions/checkout@v3
- name: Install Poetry
uses: snok/install-poetry@v1
+ with:
+ virtualenvs-create: true
+ virtualenvs-in-project: true
- name: Set up Python
uses: actions/setup-python@v4
with:
@@ -109,7 +131,7 @@ jobs:
- name: Install dependencies
run: |
poetry check --lock
- poetry install --with dev
+ poetry install --sync --with dev
- name: Check format
run: make check-format
- name: Docstring test
@@ -139,6 +161,7 @@ jobs:
name: Notebook Checks
runs-on: ubuntu-latest
strategy:
+ fail-fast: false
matrix:
notebook:
# - "Activation_Patching_in_TL_Demo"
@@ -158,28 +181,60 @@ jobs:
- "Patchscopes_Generation_Demo"
# - "T5"
steps:
+ - name: Free Disk Space
+ run: |
+ sudo rm -rf /usr/share/dotnet
+ sudo rm -rf /usr/local/lib/android
+ sudo rm -rf /opt/ghc
+ sudo rm -rf /opt/hostedtoolcache/CodeQL
+ sudo docker image prune --all --force
+ df -h
- uses: actions/checkout@v3
- name: Install Poetry
uses: snok/install-poetry@v1
+ with:
+ virtualenvs-create: true
+ virtualenvs-in-project: true
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: "3.11"
- cache: "poetry"
- - name: Re-use HuggingFace models cache
- uses: actions/cache/restore@v3
- with:
- path: ~/.cache/huggingface/hub
- key: ${{ runner.os }}-huggingface-models
+ # NOTE: Poetry cache disabled - causes huggingface-hub version conflicts
- name: Install dependencies
run: |
poetry check --lock
- poetry install --with dev,jupyter
+ poetry install --sync --with dev,jupyter
+ - name: Verify huggingface-hub version after install
+ run: |
+ VERSION=$(poetry run python -c "import huggingface_hub; print(huggingface_hub.__version__)")
+ echo "huggingface-hub version after poetry install: $VERSION"
- name: Install pandoc
uses: awalsh128/cache-apt-pkgs-action@latest
with:
packages: pandoc
version: 1.0
+ - name: Register Poetry venv as Jupyter kernel
+ run: |
+ poetry run python -m ipykernel install --user --name=poetry-env
+ - name: Ensure correct huggingface-hub version
+ run: |
+ # Force install the exact version from poetry.lock (0.33.0)
+ # transformers 4.46.3 requires huggingface-hub>=0.23.2,<1.0
+ poetry run pip install --force-reinstall --no-deps huggingface-hub==0.33.0
+ - name: Verify huggingface-hub version
+ run: |
+ VERSION=$(poetry run python -c "import huggingface_hub; print(huggingface_hub.__version__)")
+ echo "huggingface-hub version: $VERSION"
+ if [[ "$VERSION" == 1.* ]]; then
+ echo "ERROR: huggingface-hub version 1.x detected, but <1.0 is required!"
+ exit 1
+ fi
+ - name: Final version check before pytest
+ run: |
+ echo "=== Environment check ==="
+ poetry run which python
+ poetry run pip show huggingface-hub | grep Version
+ poetry run python -c "import transformers; print('transformers OK')"
- name: Check Notebook Output Consistency
# Note: currently only checks notebooks we have specifically setup for this
run: poetry run pytest --nbval-sanitize-with demos/doc_sanitize.cfg demos/${{ matrix.notebook }}.ipynb
@@ -188,9 +243,10 @@ jobs:
build-docs:
# When running on a PR, this just checks we can build the docs without errors
# When running on merge to main, it builds the docs and then another job deploys them
+ # Only runs on the original repo, not forks
name: 'Build Docs'
runs-on: ubuntu-latest
- if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/dev') || contains(github.head_ref, 'docs')
+ if: github.repository == 'TransformerLensOrg/TransformerLens' && (github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/dev') || contains(github.head_ref, 'docs'))
needs: code-checks
steps:
- uses: actions/checkout@v4
@@ -233,8 +289,8 @@ jobs:
deploy-docs:
name: Deploy Docs
runs-on: ubuntu-latest
- # Only run if merging a PR into main
- if: github.event_name == 'push' && github.ref == 'refs/heads/main'
+ # Only run if merging a PR into main on the original repo, not forks
+ if: github.repository == 'TransformerLensOrg/TransformerLens' && github.event_name == 'push' && github.ref == 'refs/heads/main'
needs: build-docs
steps:
- uses: actions/checkout@v4
diff --git a/demos/Colab_Compatibility.ipynb b/demos/Colab_Compatibility.ipynb
index f1a567b5f..74a311b97 100644
--- a/demos/Colab_Compatibility.ipynb
+++ b/demos/Colab_Compatibility.ipynb
@@ -2,8 +2,15 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {},
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2025-11-26T11:28:54.525289Z",
+ "iopub.status.busy": "2025-11-26T11:28:54.525214Z",
+ "iopub.status.idle": "2025-11-26T11:28:54.569667Z",
+ "shell.execute_reply": "2025-11-26T11:28:54.569316Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -11,16 +18,6 @@
"text": [
"Running as a Jupyter notebook - intended for development only!\n"
]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/var/folders/m3/z6c6rcdj1rbb2jh9vqpgvxg40000gn/T/ipykernel_86391/3507779555.py:18: DeprecationWarning: `magic(...)` is deprecated since IPython 0.13 (warning added in 8.1), use run_line_magic(magic_name, parameter_s).\n",
- " ipython.magic(\"load_ext autoreload\")\n",
- "/var/folders/m3/z6c6rcdj1rbb2jh9vqpgvxg40000gn/T/ipykernel_86391/3507779555.py:19: DeprecationWarning: `magic(...)` is deprecated since IPython 0.13 (warning added in 8.1), use run_line_magic(magic_name, parameter_s).\n",
- " ipython.magic(\"autoreload 2\")\n"
- ]
}
],
"source": [
@@ -41,8 +38,8 @@
"\n",
" ipython = get_ipython()\n",
" # Code to automatically update the HookedTransformer code as its edited without restarting the kernel\n",
- " ipython.magic(\"load_ext autoreload\")\n",
- " ipython.magic(\"autoreload 2\")\n",
+ " ipython.run_line_magic(\"load_ext\", \"autoreload\")\n",
+ " ipython.run_line_magic(\"autoreload\", \"2\")\n",
"\n",
"\n",
"\n",
@@ -58,18 +55,26 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {},
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2025-11-26T11:28:54.582413Z",
+ "iopub.status.busy": "2025-11-26T11:28:54.582330Z",
+ "iopub.status.idle": "2025-11-26T11:28:57.281038Z",
+ "shell.execute_reply": "2025-11-26T11:28:57.280640Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "TransformerLens currently supports 216 models out of the box.\n"
+ "TransformerLens currently supports 230 models out of the box.\n"
]
}
],
"source": [
+ "# NBVAL_IGNORE_OUTPUT\n",
"import torch\n",
"from transformer_lens import HookedTransformer, HookedEncoderDecoder, HookedEncoder, BertNextSentencePrediction, loading\n",
"from transformers import AutoTokenizer, LlamaForCausalLM, LlamaTokenizer\n",
@@ -88,8 +93,15 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {},
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2025-11-26T11:28:57.282293Z",
+ "iopub.status.busy": "2025-11-26T11:28:57.282164Z",
+ "iopub.status.idle": "2025-11-26T11:28:57.312622Z",
+ "shell.execute_reply": "2025-11-26T11:28:57.312254Z"
+ }
+ },
"outputs": [],
"source": [
"def mark_models_as_tested(model_set: List[str]) -> None:\n",
@@ -198,8 +210,15 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {},
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2025-11-26T11:28:57.313778Z",
+ "iopub.status.busy": "2025-11-26T11:28:57.313719Z",
+ "iopub.status.idle": "2025-11-26T11:28:57.328306Z",
+ "shell.execute_reply": "2025-11-26T11:28:57.327890Z"
+ }
+ },
"outputs": [],
"source": [
"# The following models can run in the T4 free environment\n",
@@ -324,8 +343,15 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {},
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2025-11-26T11:28:57.329256Z",
+ "iopub.status.busy": "2025-11-26T11:28:57.329199Z",
+ "iopub.status.idle": "2025-11-26T11:28:57.342129Z",
+ "shell.execute_reply": "2025-11-26T11:28:57.341819Z"
+ }
+ },
"outputs": [],
"source": [
"paid_gpu_models = [\n",
@@ -395,8 +421,15 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "metadata": {},
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2025-11-26T11:28:57.343144Z",
+ "iopub.status.busy": "2025-11-26T11:28:57.343091Z",
+ "iopub.status.idle": "2025-11-26T11:28:57.355679Z",
+ "shell.execute_reply": "2025-11-26T11:28:57.355327Z"
+ }
+ },
"outputs": [],
"source": [
"paid_cpu_models = [\n",
@@ -428,8 +461,15 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {},
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2025-11-26T11:28:57.356618Z",
+ "iopub.status.busy": "2025-11-26T11:28:57.356568Z",
+ "iopub.status.idle": "2025-11-26T11:28:57.368634Z",
+ "shell.execute_reply": "2025-11-26T11:28:57.368343Z"
+ }
+ },
"outputs": [],
"source": [
"incompatible_models = [\n",
@@ -460,8 +500,15 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "metadata": {},
+ "execution_count": 8,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2025-11-26T11:28:57.369547Z",
+ "iopub.status.busy": "2025-11-26T11:28:57.369494Z",
+ "iopub.status.idle": "2025-11-26T11:28:57.380774Z",
+ "shell.execute_reply": "2025-11-26T11:28:57.380401Z"
+ }
+ },
"outputs": [],
"source": [
"# The following models take a few extra steps to function. Check the official demo for more\n",
@@ -482,8 +529,15 @@
},
{
"cell_type": "code",
- "execution_count": 11,
- "metadata": {},
+ "execution_count": 9,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2025-11-26T11:28:57.381650Z",
+ "iopub.status.busy": "2025-11-26T11:28:57.381607Z",
+ "iopub.status.idle": "2025-11-26T11:28:57.392857Z",
+ "shell.execute_reply": "2025-11-26T11:28:57.392541Z"
+ }
+ },
"outputs": [],
"source": [
"# These all work on the free version of Colab\n",
@@ -500,8 +554,15 @@
},
{
"cell_type": "code",
- "execution_count": 12,
- "metadata": {},
+ "execution_count": 10,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2025-11-26T11:28:57.393724Z",
+ "iopub.status.busy": "2025-11-26T11:28:57.393675Z",
+ "iopub.status.idle": "2025-11-26T11:28:57.405139Z",
+ "shell.execute_reply": "2025-11-26T11:28:57.404728Z"
+ }
+ },
"outputs": [],
"source": [
"# This model works on the free version of Colab\n",
@@ -520,25 +581,53 @@
},
{
"cell_type": "code",
- "execution_count": 13,
- "metadata": {},
+ "execution_count": 11,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2025-11-26T11:28:57.405948Z",
+ "iopub.status.busy": "2025-11-26T11:28:57.405904Z",
+ "iopub.status.idle": "2025-11-26T11:28:57.419836Z",
+ "shell.execute_reply": "2025-11-26T11:28:57.419415Z"
+ }
+ },
"outputs": [],
"source": [
"broken_models = [\n",
" \"Baidicoot/Othello-GPT-Transformer-Lens\",\n",
- "]"
+ "]\n",
+ "mark_models_as_tested(broken_models)"
]
},
{
"cell_type": "code",
- "execution_count": 14,
- "metadata": {},
+ "execution_count": 12,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2025-11-26T11:28:57.420736Z",
+ "iopub.status.busy": "2025-11-26T11:28:57.420686Z",
+ "iopub.status.idle": "2025-11-26T11:28:57.432784Z",
+ "shell.execute_reply": "2025-11-26T11:28:57.432523Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Baidicoot/Othello-GPT-Transformer-Lens\n"
+ "google/gemma-3-270m\n",
+ "google/gemma-3-270m-it\n",
+ "google/gemma-3-1b-pt\n",
+ "google/gemma-3-1b-it\n",
+ "google/gemma-3-4b-pt\n",
+ "google/gemma-3-4b-it\n",
+ "google/gemma-3-12b-pt\n",
+ "google/gemma-3-12b-it\n",
+ "google/gemma-3-27b-pt\n",
+ "google/gemma-3-27b-it\n",
+ "google/medgemma-4b-pt\n",
+ "google/medgemma-4b-it\n",
+ "google/medgemma-27b-it\n",
+ "google/medgemma-27b-text-it\n"
]
}
],
@@ -566,7 +655,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.7"
+ "version": "3.11.14"
}
},
"nbformat": 4,
diff --git a/demos/Interactive_Neuroscope.ipynb b/demos/Interactive_Neuroscope.ipynb
index e6999f977..e7f611ab3 100644
--- a/demos/Interactive_Neuroscope.ipynb
+++ b/demos/Interactive_Neuroscope.ipynb
@@ -1,482 +1,483 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
\n",
- ""
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Interactive Neuroscope\n",
- "\n",
- "*This is an interactive accompaniment to [neuroscope.io](https://neuroscope.io) and to the [studying learned language features post](https://www.alignmentforum.org/posts/Qup9gorqpd9qKAEav/200-cop-in-mi-studying-learned-features-in-language-models) in [200 Concrete Open Problems in Mechanistic Interpretability](https://neelnanda.io/concrete-open-problems)*\n",
- "\n",
- "There's a surprisingly rich ecosystem of easy ways to create interactive graphics, especially for ML systems. If you're trying to do mechanistic interpretability, the ability to do web dev and to both visualize data and interact with it seems high value! \n",
- "\n",
- "This is a demo of how you can combine HookedTransformer and [Gradio](https://gradio.app/) to create an interactive Neuroscope - a visualization of a neuron's activations on text that will dynamically update as you edit the text. I don't particularly claim that this code is any *good*, but the goal is to illustrate what quickly hacking together a custom visualisation (while knowing fuck all about web dev, like me) can look like! (And as such, I try to explain the basic web dev concepts I use)\n",
- "\n",
- "Note that you'll need to run the code yourself to get the interactive interface, so the cell at the bottom will be blank at first!\n",
- "\n",
- "To emphasise - the point of this notebook is to be a rough proof of concept that just about works, *not* to be the well executed ideal of interactively studying neurons! You are highly encouraged to write your own (and ideally, to [make a pull request](https://github.com/neelnanda-io/TransformerLens/pulls) with improvements!)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Running as a Jupyter notebook - intended for development only!\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/var/folders/m3/z6c6rcdj1rbb2jh9vqpgvxg40000gn/T/ipykernel_63049/1105475986.py:19: DeprecationWarning: `magic(...)` is deprecated since IPython 0.13 (warning added in 8.1), use run_line_magic(magic_name, parameter_s).\n",
- " ipython.magic(\"load_ext autoreload\")\n",
- "/var/folders/m3/z6c6rcdj1rbb2jh9vqpgvxg40000gn/T/ipykernel_63049/1105475986.py:20: DeprecationWarning: `magic(...)` is deprecated since IPython 0.13 (warning added in 8.1), use run_line_magic(magic_name, parameter_s).\n",
- " ipython.magic(\"autoreload 2\")\n"
- ]
- }
- ],
- "source": [
- "# NBVAL_IGNORE_OUTPUT\n",
- "# Janky code to do different setup when run in a Colab notebook vs VSCode\n",
- "import os\n",
- "\n",
- "DEVELOPMENT_MODE = True\n",
- "IN_GITHUB = os.getenv(\"GITHUB_ACTIONS\") == \"true\"\n",
- "try:\n",
- " import google.colab\n",
- "\n",
- " IN_COLAB = True\n",
- " print(\"Running as a Colab notebook\")\n",
- "except:\n",
- " IN_COLAB = False\n",
- " print(\"Running as a Jupyter notebook - intended for development only!\")\n",
- " from IPython import get_ipython\n",
- "\n",
- " ipython = get_ipython()\n",
- " # Code to automatically update the HookedTransformer code as its edited without restarting the kernel\n",
- " ipython.magic(\"load_ext autoreload\")\n",
- " ipython.magic(\"autoreload 2\")\n",
- "\n",
- "if IN_COLAB or IN_GITHUB:\n",
- " %pip install transformer_lens\n",
- " %pip install gradio\n",
- " %pip install datasets==2.19.1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import gradio as gr\n",
- "from transformer_lens import HookedTransformer\n",
- "from transformer_lens.utils import to_numpy\n",
- "from IPython.display import HTML"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Extracting Model Activations\n",
- "\n",
- "We first write some code using HookedTransformer's cache to extract the neuron activations on a given layer and neuron, for a given text"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loaded pretrained model gpt2-small into HookedTransformer\n"
- ]
- }
- ],
- "source": [
- "# NBVAL_IGNORE_OUTPUT\n",
- "model_name = \"gpt2-small\"\n",
- "model = HookedTransformer.from_pretrained(model_name)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_neuron_acts(text, layer, neuron_index):\n",
- " # Hacky way to get out state from a single hook - we have a single element list and edit that list within the hook.\n",
- " cache = {}\n",
- "\n",
- " def caching_hook(act, hook):\n",
- " cache[\"activation\"] = act[0, :, neuron_index]\n",
- "\n",
- " model.run_with_hooks(\n",
- " text, fwd_hooks=[(f\"blocks.{layer}.mlp.hook_post\", caching_hook)]\n",
- " )\n",
- " return to_numpy(cache[\"activation\"])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can run this function and verify that it gives vaguely sensible outputs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['<|endoftext|>', 'The', ' following', ' is', ' a', ' list', ' of', ' powers', ' of', ' 10', ':', ' 1', ',', ' 10', ',', ' 100', ',', ' 1000', ',', ' 10000', ',', ' 100', '000', ',', ' 100', '0000', ',', ' 100', '00000']\n"
- ]
- }
- ],
- "source": [
- "default_layer = 9\n",
- "default_neuron_index = 652\n",
- "default_text = \"The following is a list of powers of 10: 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000\"\n",
- "print(model.to_str_tokens(default_text))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[-0.08643512 -0.14071988 -0.1039816 -0.12390755 -0.04058974 -0.11064906\n",
- " -0.05189841 -0.1127614 -0.0690546 -0.11189383 -0.030592 -0.10336886\n",
- " -0.04322351 1.5935613 -0.14205799 2.511661 -0.1331642 2.5196698\n",
- " -0.11360849 3.076527 -0.11637434 0.5393868 2.3499725 -0.14952122\n",
- " -0.16476354 1.944909 -0.13690136 -0.08802476 2.184888 ]\n"
- ]
- }
- ],
- "source": [
- "# NBVAL_IGNORE_OUTPUT\n",
- "print(get_neuron_acts(default_text, default_layer, default_neuron_index))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Visualizing Model Activations\n",
- "\n",
- "We now write some code to visualize the neuron activations on some text - we're going to hack something together which just does some string processing to make an HTML string, with each token element colored according to the intensity neuron activation. We normalize the neuron activations so they all lie in [0, 1]. You can do much better, but this is a useful proof of concept of what \"just hack stuff together\" can look like!\n",
- "\n",
- "I'll be keeping neuron 562 in layer 9 as a running example, as it seems to activate strongly on powers of 10.\n",
- "\n",
- "Note that this visualization is very sensitive to `max_val` and `min_val`! You can tune those to whatever seems reasonable for the distribution of neuron activations you care about - I generally default to `min_val=0` and `max_val` as the max activation across the dataset."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "# This is some CSS (tells us what style )to give each token a thin gray border, to make it easy to see token separation\n",
- "style_string = \"\"\"\"\"\"\n",
- "\n",
- "\n",
- "def calculate_color(val, max_val, min_val):\n",
- " # Hacky code that takes in a value val in range [min_val, max_val], normalizes it to [0, 1] and returns a color which interpolates between slightly off-white and red (0 = white, 1 = red)\n",
- " # We return a string of the form \"rgb(240, 240, 240)\" which is a color CSS knows\n",
- " normalized_val = (val - min_val) / max_val\n",
- " return f\"rgb(240, {240*(1-normalized_val)}, {240*(1-normalized_val)})\"\n",
- "\n",
- "\n",
- "def basic_neuron_vis(text, layer, neuron_index, max_val=None, min_val=None):\n",
- " \"\"\"\n",
- " text: The text to visualize\n",
- " layer: The layer index\n",
- " neuron_index: The neuron index\n",
- " max_val: The top end of our activation range, defaults to the maximum activation\n",
- " min_val: The top end of our activation range, defaults to the minimum activation\n",
- "\n",
- " Returns a string of HTML that displays the text with each token colored according to its activation\n",
- "\n",
- " Note: It's useful to be able to input a fixed max_val and min_val, because otherwise the colors will change as you edit the text, which is annoying.\n",
- " \"\"\"\n",
- " if layer is None:\n",
- " return \"Please select a Layer\"\n",
- " if neuron_index is None:\n",
- " return \"Please select a Neuron\"\n",
- " acts = get_neuron_acts(text, layer, neuron_index)\n",
- " act_max = acts.max()\n",
- " act_min = acts.min()\n",
- " # Defaults to the max and min of the activations\n",
- " if max_val is None:\n",
- " max_val = act_max\n",
- " if min_val is None:\n",
- " min_val = act_min\n",
- " # We want to make a list of HTML strings to concatenate into our final HTML string\n",
- " # We first add the style to make each token element have a nice border\n",
- " htmls = [style_string]\n",
- " # We then add some text to tell us what layer and neuron we're looking at - we're just dealing with strings and can use f-strings as normal\n",
- " # h4 means \"small heading\"\n",
- " htmls.append(f\"Layer: {layer}. Neuron Index: {neuron_index}
\")\n",
- " # We then add a line telling us the limits of our range\n",
- " htmls.append(\n",
- " f\"Max Range: {max_val:.4f}. Min Range: {min_val:.4f}
\"\n",
- " )\n",
- " # If we added a custom range, print a line telling us the range of our activations too.\n",
- " if act_max != max_val or act_min != min_val:\n",
- " htmls.append(\n",
- " f\"Custom Range Set. Max Act: {act_max:.4f}. Min Act: {act_min:.4f}
\"\n",
- " )\n",
- " # Convert the text to a list of tokens\n",
- " str_tokens = model.to_str_tokens(text)\n",
- " for tok, act in zip(str_tokens, acts):\n",
- " # A span is an HTML element that lets us style a part of a string (and remains on the same line by default)\n",
- " # We set the background color of the span to be the color we calculated from the activation\n",
- " # We set the contents of the span to be the token\n",
- " htmls.append(\n",
- " f\"{tok}\"\n",
- " )\n",
- "\n",
- " return \"\".join(htmls)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Displayed HTML\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "Layer: 9. Neuron Index: 652
Max Range: 4.0000. Min Range: 0.0000
Custom Range Set. Max Act: 3.0765. Min Act: -0.1648
<|endoftext|>The following is a list of powers of 10: 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "HTML String - it's just raw HTML code!\n",
- "Layer: 9. Neuron Index: 652
Max Range: 4.0000. Min Range: 0.0000
Custom Range Set. Max Act: 3.0765. Min Act: -0.1648
<|endoftext|>The following is a list of powers of 10: 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000\n"
- ]
- }
- ],
- "source": [
- "# NBVAL_IGNORE_OUTPUT\n",
- "# The function outputs a string of HTML\n",
- "default_max_val = 4.0\n",
- "default_min_val = 0.0\n",
- "default_html_string = basic_neuron_vis(\n",
- " default_text,\n",
- " default_layer,\n",
- " default_neuron_index,\n",
- " max_val=default_max_val,\n",
- " min_val=default_min_val,\n",
- ")\n",
- "\n",
- "# IPython lets us display HTML\n",
- "print(\"Displayed HTML\")\n",
- "display(HTML(default_html_string))\n",
- "\n",
- "# We can also print the string directly\n",
- "print(\"HTML String - it's just raw HTML code!\")\n",
- "print(default_html_string)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Create Interactive UI\n",
- "\n",
- "We now put all these together to create an interactive visualization in Gradio! \n",
- "\n",
- "The internal format is that there's a bunch of elements - Textboxes, Numbers, etc which the user can interact with and which return strings and numbers. And we can also define output elements that just display things - in this case, one which takes in an arbitrary HTML string. We call `input.change(update_function, inputs, output)` - this says \"if that input element changes, run the update function on the value of each of the elements in `inputs` and set the value of `output` to the output of the function\". As a bonus, this gives us live interactivity!\n",
- "\n",
- "This is also more complex than a typical Gradio intro example - I wanted to use custom HTML to display the nice colours, which made things much messier! Normally you could just make `out` into another Textbox and pass it a string."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "# The `with gr.Blocks() as demo:` syntax just creates a variable called demo containing all these components\n",
- "with gr.Blocks() as demo:\n",
- " gr.HTML(value=f\"Hacky Interactive Neuroscope for {model_name}\")\n",
- " # The input elements\n",
- " with gr.Row():\n",
- " with gr.Column():\n",
- " text = gr.Textbox(label=\"Text\", value=default_text)\n",
- " # Precision=0 makes it an int, otherwise it's a float\n",
- " # Value sets the initial default value\n",
- " layer = gr.Number(label=\"Layer\", value=default_layer, precision=0)\n",
- " neuron_index = gr.Number(\n",
- " label=\"Neuron Index\", value=default_neuron_index, precision=0\n",
- " )\n",
- " # If empty, these two map to None\n",
- " max_val = gr.Number(label=\"Max Value\", value=default_max_val)\n",
- " min_val = gr.Number(label=\"Min Value\", value=default_min_val)\n",
- " inputs = [text, layer, neuron_index, max_val, min_val]\n",
- " with gr.Column():\n",
- " # The output element\n",
- " out = gr.HTML(label=\"Neuron Acts\", value=default_html_string)\n",
- " for inp in inputs:\n",
- " inp.change(basic_neuron_vis, inputs, out)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can now launch our demo element, and we're done! The setting share=True even gives you a public link to the demo (though it just redirects to the backend run by this notebook, and will go away once you turn the notebook off!) Sharing makes it much slower, and can be turned off if you aren't in a colab.\n",
- "\n",
- "**Exercise:** Explore where this neuron does and does not activate. Is it just powers of ten? Just comma separated numbers? Numbers in any particular sequence?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Running on local URL: http://127.0.0.1:7860\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
- "To disable this warning, you can either:\n",
- "\t- Avoid using `tokenizers` before the fork if possible\n",
- "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Running on public URL: https://7a615281b36111d2e4.gradio.live\n",
- "\n",
- "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
- ]
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": []
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# NBVAL_IGNORE_OUTPUT\n",
- "demo.launch(share=True, height=1000)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.7.13 ('base')",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.9"
- },
- "orig_nbformat": 4,
- "vscode": {
- "interpreter": {
- "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe"
- }
- }
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ " \n",
+ ""
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Interactive Neuroscope\n",
+ "\n",
+ "*This is an interactive accompaniment to [neuroscope.io](https://neuroscope.io) and to the [studying learned language features post](https://www.alignmentforum.org/posts/Qup9gorqpd9qKAEav/200-cop-in-mi-studying-learned-features-in-language-models) in [200 Concrete Open Problems in Mechanistic Interpretability](https://neelnanda.io/concrete-open-problems)*\n",
+ "\n",
+ "There's a surprisingly rich ecosystem of easy ways to create interactive graphics, especially for ML systems. If you're trying to do mechanistic interpretability, the ability to do web dev and to both visualize data and interact with it seems high value! \n",
+ "\n",
+ "This is a demo of how you can combine HookedTransformer and [Gradio](https://gradio.app/) to create an interactive Neuroscope - a visualization of a neuron's activations on text that will dynamically update as you edit the text. I don't particularly claim that this code is any *good*, but the goal is to illustrate what quickly hacking together a custom visualisation (while knowing fuck all about web dev, like me) can look like! (And as such, I try to explain the basic web dev concepts I use)\n",
+ "\n",
+ "Note that you'll need to run the code yourself to get the interactive interface, so the cell at the bottom will be blank at first!\n",
+ "\n",
+ "To emphasise - the point of this notebook is to be a rough proof of concept that just about works, *not* to be the well executed ideal of interactively studying neurons! You are highly encouraged to write your own (and ideally, to [make a pull request](https://github.com/neelnanda-io/TransformerLens/pulls) with improvements!)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running as a Jupyter notebook - intended for development only!\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 2
-}
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/m3/z6c6rcdj1rbb2jh9vqpgvxg40000gn/T/ipykernel_63049/1105475986.py:19: DeprecationWarning: `magic(...)` is deprecated since IPython 0.13 (warning added in 8.1), use run_line_magic(magic_name, parameter_s).\n",
+ " ipython.magic(\"load_ext autoreload\")\n",
+ "/var/folders/m3/z6c6rcdj1rbb2jh9vqpgvxg40000gn/T/ipykernel_63049/1105475986.py:20: DeprecationWarning: `magic(...)` is deprecated since IPython 0.13 (warning added in 8.1), use run_line_magic(magic_name, parameter_s).\n",
+ " ipython.magic(\"autoreload 2\")\n"
+ ]
+ }
+ ],
+ "source": [
+ "# NBVAL_IGNORE_OUTPUT\n",
+ "# Janky code to do different setup when run in a Colab notebook vs VSCode\n",
+ "import os\n",
+ "\n",
+ "DEVELOPMENT_MODE = True\n",
+ "IN_GITHUB = os.getenv(\"GITHUB_ACTIONS\") == \"true\"\n",
+ "try:\n",
+ " import google.colab\n",
+ "\n",
+ " IN_COLAB = True\n",
+ " print(\"Running as a Colab notebook\")\n",
+ "except:\n",
+ " IN_COLAB = False\n",
+ " print(\"Running as a Jupyter notebook - intended for development only!\")\n",
+ " from IPython import get_ipython\n",
+ "\n",
+ " ipython = get_ipython()\n",
+ " # Code to automatically update the HookedTransformer code as its edited without restarting the kernel\n",
+ " ipython.run_line_magic(\"load_ext\", \"autoreload\")\n",
+ " ipython.run_line_magic(\"autoreload\", \"2\")\n",
+ "\n",
+ "if IN_COLAB or IN_GITHUB:\n",
+ " %pip install transformer_lens\n",
+ " %pip install gradio\n",
+ " %pip install datasets==2.19.1\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NBVAL_IGNORE_OUTPUT\n",
+ "import gradio as gr\n",
+ "from transformer_lens import HookedTransformer\n",
+ "from transformer_lens.utils import to_numpy\n",
+ "from IPython.display import HTML"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Extracting Model Activations\n",
+ "\n",
+ "We first write some code using HookedTransformer's cache to extract the neuron activations on a given layer and neuron, for a given text"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded pretrained model gpt2-small into HookedTransformer\n"
+ ]
+ }
+ ],
+ "source": [
+ "# NBVAL_IGNORE_OUTPUT\n",
+ "model_name = \"gpt2-small\"\n",
+ "model = HookedTransformer.from_pretrained(model_name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_neuron_acts(text, layer, neuron_index):\n",
+ " # Hacky way to get out state from a single hook - we have a single element list and edit that list within the hook.\n",
+ " cache = {}\n",
+ "\n",
+ " def caching_hook(act, hook):\n",
+ " cache[\"activation\"] = act[0, :, neuron_index]\n",
+ "\n",
+ " model.run_with_hooks(\n",
+ " text, fwd_hooks=[(f\"blocks.{layer}.mlp.hook_post\", caching_hook)]\n",
+ " )\n",
+ " return to_numpy(cache[\"activation\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can run this function and verify that it gives vaguely sensible outputs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['<|endoftext|>', 'The', ' following', ' is', ' a', ' list', ' of', ' powers', ' of', ' 10', ':', ' 1', ',', ' 10', ',', ' 100', ',', ' 1000', ',', ' 10000', ',', ' 100', '000', ',', ' 100', '0000', ',', ' 100', '00000']\n"
+ ]
+ }
+ ],
+ "source": [
+ "default_layer = 9\n",
+ "default_neuron_index = 652\n",
+ "default_text = \"The following is a list of powers of 10: 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000\"\n",
+ "print(model.to_str_tokens(default_text))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-0.08643512 -0.14071988 -0.1039816 -0.12390755 -0.04058974 -0.11064906\n",
+ " -0.05189841 -0.1127614 -0.0690546 -0.11189383 -0.030592 -0.10336886\n",
+ " -0.04322351 1.5935613 -0.14205799 2.511661 -0.1331642 2.5196698\n",
+ " -0.11360849 3.076527 -0.11637434 0.5393868 2.3499725 -0.14952122\n",
+ " -0.16476354 1.944909 -0.13690136 -0.08802476 2.184888 ]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# NBVAL_IGNORE_OUTPUT\n",
+ "print(get_neuron_acts(default_text, default_layer, default_neuron_index))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualizing Model Activations\n",
+ "\n",
+ "We now write some code to visualize the neuron activations on some text - we're going to hack something together which just does some string processing to make an HTML string, with each token element colored according to the intensity neuron activation. We normalize the neuron activations so they all lie in [0, 1]. You can do much better, but this is a useful proof of concept of what \"just hack stuff together\" can look like!\n",
+ "\n",
+ "I'll be keeping neuron 562 in layer 9 as a running example, as it seems to activate strongly on powers of 10.\n",
+ "\n",
+ "Note that this visualization is very sensitive to `max_val` and `min_val`! You can tune those to whatever seems reasonable for the distribution of neuron activations you care about - I generally default to `min_val=0` and `max_val` as the max activation across the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# This is some CSS (tells us what style )to give each token a thin gray border, to make it easy to see token separation\n",
+ "style_string = \"\"\"\"\"\"\n",
+ "\n",
+ "\n",
+ "def calculate_color(val, max_val, min_val):\n",
+ " # Hacky code that takes in a value val in range [min_val, max_val], normalizes it to [0, 1] and returns a color which interpolates between slightly off-white and red (0 = white, 1 = red)\n",
+ " # We return a string of the form \"rgb(240, 240, 240)\" which is a color CSS knows\n",
+ " normalized_val = (val - min_val) / max_val\n",
+ " return f\"rgb(240, {240*(1-normalized_val)}, {240*(1-normalized_val)})\"\n",
+ "\n",
+ "\n",
+ "def basic_neuron_vis(text, layer, neuron_index, max_val=None, min_val=None):\n",
+ " \"\"\"\n",
+ " text: The text to visualize\n",
+ " layer: The layer index\n",
+ " neuron_index: The neuron index\n",
+ " max_val: The top end of our activation range, defaults to the maximum activation\n",
+ " min_val: The top end of our activation range, defaults to the minimum activation\n",
+ "\n",
+ " Returns a string of HTML that displays the text with each token colored according to its activation\n",
+ "\n",
+ " Note: It's useful to be able to input a fixed max_val and min_val, because otherwise the colors will change as you edit the text, which is annoying.\n",
+ " \"\"\"\n",
+ " if layer is None:\n",
+ " return \"Please select a Layer\"\n",
+ " if neuron_index is None:\n",
+ " return \"Please select a Neuron\"\n",
+ " acts = get_neuron_acts(text, layer, neuron_index)\n",
+ " act_max = acts.max()\n",
+ " act_min = acts.min()\n",
+ " # Defaults to the max and min of the activations\n",
+ " if max_val is None:\n",
+ " max_val = act_max\n",
+ " if min_val is None:\n",
+ " min_val = act_min\n",
+ " # We want to make a list of HTML strings to concatenate into our final HTML string\n",
+ " # We first add the style to make each token element have a nice border\n",
+ " htmls = [style_string]\n",
+ " # We then add some text to tell us what layer and neuron we're looking at - we're just dealing with strings and can use f-strings as normal\n",
+ " # h4 means \"small heading\"\n",
+ " htmls.append(f\"Layer: {layer}. Neuron Index: {neuron_index}
\")\n",
+ " # We then add a line telling us the limits of our range\n",
+ " htmls.append(\n",
+ " f\"Max Range: {max_val:.4f}. Min Range: {min_val:.4f}
\"\n",
+ " )\n",
+ " # If we added a custom range, print a line telling us the range of our activations too.\n",
+ " if act_max != max_val or act_min != min_val:\n",
+ " htmls.append(\n",
+ " f\"Custom Range Set. Max Act: {act_max:.4f}. Min Act: {act_min:.4f}
\"\n",
+ " )\n",
+ " # Convert the text to a list of tokens\n",
+ " str_tokens = model.to_str_tokens(text)\n",
+ " for tok, act in zip(str_tokens, acts):\n",
+ " # A span is an HTML element that lets us style a part of a string (and remains on the same line by default)\n",
+ " # We set the background color of the span to be the color we calculated from the activation\n",
+ " # We set the contents of the span to be the token\n",
+ " htmls.append(\n",
+ " f\"{tok}\"\n",
+ " )\n",
+ "\n",
+ " return \"\".join(htmls)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Displayed HTML\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "Layer: 9. Neuron Index: 652
Max Range: 4.0000. Min Range: 0.0000
Custom Range Set. Max Act: 3.0765. Min Act: -0.1648
<|endoftext|>The following is a list of powers of 10: 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "HTML String - it's just raw HTML code!\n",
+ "Layer: 9. Neuron Index: 652
Max Range: 4.0000. Min Range: 0.0000
Custom Range Set. Max Act: 3.0765. Min Act: -0.1648
<|endoftext|>The following is a list of powers of 10: 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000\n"
+ ]
+ }
+ ],
+ "source": [
+ "# NBVAL_IGNORE_OUTPUT\n",
+ "# The function outputs a string of HTML\n",
+ "default_max_val = 4.0\n",
+ "default_min_val = 0.0\n",
+ "default_html_string = basic_neuron_vis(\n",
+ " default_text,\n",
+ " default_layer,\n",
+ " default_neuron_index,\n",
+ " max_val=default_max_val,\n",
+ " min_val=default_min_val,\n",
+ ")\n",
+ "\n",
+ "# IPython lets us display HTML\n",
+ "print(\"Displayed HTML\")\n",
+ "display(HTML(default_html_string))\n",
+ "\n",
+ "# We can also print the string directly\n",
+ "print(\"HTML String - it's just raw HTML code!\")\n",
+ "print(default_html_string)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create Interactive UI\n",
+ "\n",
+ "We now put all these together to create an interactive visualization in Gradio! \n",
+ "\n",
+ "The internal format is that there's a bunch of elements - Textboxes, Numbers, etc which the user can interact with and which return strings and numbers. And we can also define output elements that just display things - in this case, one which takes in an arbitrary HTML string. We call `input.change(update_function, inputs, output)` - this says \"if that input element changes, run the update function on the value of each of the elements in `inputs` and set the value of `output` to the output of the function\". As a bonus, this gives us live interactivity!\n",
+ "\n",
+ "This is also more complex than a typical Gradio intro example - I wanted to use custom HTML to display the nice colours, which made things much messier! Normally you could just make `out` into another Textbox and pass it a string."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The `with gr.Blocks() as demo:` syntax just creates a variable called demo containing all these components\n",
+ "with gr.Blocks() as demo:\n",
+ " gr.HTML(value=f\"Hacky Interactive Neuroscope for {model_name}\")\n",
+ " # The input elements\n",
+ " with gr.Row():\n",
+ " with gr.Column():\n",
+ " text = gr.Textbox(label=\"Text\", value=default_text)\n",
+ " # Precision=0 makes it an int, otherwise it's a float\n",
+ " # Value sets the initial default value\n",
+ " layer = gr.Number(label=\"Layer\", value=default_layer, precision=0)\n",
+ " neuron_index = gr.Number(\n",
+ " label=\"Neuron Index\", value=default_neuron_index, precision=0\n",
+ " )\n",
+ " # If empty, these two map to None\n",
+ " max_val = gr.Number(label=\"Max Value\", value=default_max_val)\n",
+ " min_val = gr.Number(label=\"Min Value\", value=default_min_val)\n",
+ " inputs = [text, layer, neuron_index, max_val, min_val]\n",
+ " with gr.Column():\n",
+ " # The output element\n",
+ " out = gr.HTML(label=\"Neuron Acts\", value=default_html_string)\n",
+ " for inp in inputs:\n",
+ " inp.change(basic_neuron_vis, inputs, out)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now launch our demo element, and we're done! The setting share=True even gives you a public link to the demo (though it just redirects to the backend run by this notebook, and will go away once you turn the notebook off!) Sharing makes it much slower, and can be turned off if you aren't in a colab.\n",
+ "\n",
+ "**Exercise:** Explore where this neuron does and does not activate. Is it just powers of ten? Just comma separated numbers? Numbers in any particular sequence?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running on local URL: http://127.0.0.1:7860\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+ "To disable this warning, you can either:\n",
+ "\t- Avoid using `tokenizers` before the fork if possible\n",
+ "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running on public URL: https://7a615281b36111d2e4.gradio.live\n",
+ "\n",
+ "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": []
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# NBVAL_IGNORE_OUTPUT\n",
+ "demo.launch(share=True, height=1000)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.7.13 ('base')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.9"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 62bb88aac..322110026 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
[[package]]
name = "accelerate"
@@ -6,6 +6,7 @@ version = "1.0.1"
description = "Accelerate"
optional = false
python-versions = ">=3.8.0"
+groups = ["main"]
files = [
{file = "accelerate-1.0.1-py3-none-any.whl", hash = "sha256:c6aa0c7b8a797cb150471e90e3ca36ac41f5d4b40512cdd6f058b8bf25589467"},
{file = "accelerate-1.0.1.tar.gz", hash = "sha256:e8f95fc2db14915dc0a9182edfcf3068e5ddb2fa310b583717ad44e5c442399c"},
@@ -31,12 +32,39 @@ test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "py
test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"]
testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+[[package]]
+name = "aiofiles"
+version = "23.2.1"
+description = "File support for asyncio."
+optional = false
+python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version < \"3.10\""
+files = [
+ {file = "aiofiles-23.2.1-py3-none-any.whl", hash = "sha256:19297512c647d4b27a2cf7c34caa7e405c0d60b5560618a29a9fe027b18b0107"},
+ {file = "aiofiles-23.2.1.tar.gz", hash = "sha256:84ec2218d8419404abcb9f0c02df3f34c6e0a68ed41072acfb1cef5cbc29051a"},
+]
+
+[[package]]
+name = "aiofiles"
+version = "24.1.0"
+description = "File support for asyncio."
+optional = false
+python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.10\""
+files = [
+ {file = "aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"},
+ {file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"},
+]
+
[[package]]
name = "aiohappyeyeballs"
version = "2.4.4"
description = "Happy Eyeballs for asyncio"
optional = false
python-versions = ">=3.8"
+groups = ["main"]
files = [
{file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"},
{file = "aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745"},
@@ -48,6 +76,7 @@ version = "3.10.11"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.8"
+groups = ["main"]
files = [
{file = "aiohttp-3.10.11-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:5077b1a5f40ffa3ba1f40d537d3bec4383988ee51fbba6b74aa8fb1bc466599e"},
{file = "aiohttp-3.10.11-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8d6a14a4d93b5b3c2891fca94fa9d41b2322a68194422bef0dd5ec1e57d7d298"},
@@ -152,7 +181,7 @@ multidict = ">=4.5,<7.0"
yarl = ">=1.12.0,<2.0"
[package.extras]
-speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
+speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.2.0) ; sys_platform == \"linux\" or sys_platform == \"darwin\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
[[package]]
name = "aiosignal"
@@ -160,6 +189,7 @@ version = "1.3.1"
description = "aiosignal: a list of registered asynchronous callbacks"
optional = false
python-versions = ">=3.7"
+groups = ["main"]
files = [
{file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"},
{file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"},
@@ -174,17 +204,31 @@ version = "0.7.13"
description = "A configurable sidebar-enabled Sphinx theme"
optional = false
python-versions = ">=3.6"
+groups = ["docs"]
files = [
{file = "alabaster-0.7.13-py3-none-any.whl", hash = "sha256:1ee19aca801bbabb5ba3f5f258e4422dfa86f82f3e9cefb0859b283cdd7f62a3"},
{file = "alabaster-0.7.13.tar.gz", hash = "sha256:a27a4a084d5e690e16e01e03ad2b2e552c61a65469419b907243193de1a84ae2"},
]
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+description = "Document parameters, class attributes, return types, and variables inline, with Annotated."
+optional = false
+python-versions = ">=3.8"
+groups = ["dev"]
+files = [
+ {file = "annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320"},
+ {file = "annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4"},
+]
+
[[package]]
name = "annotated-types"
version = "0.7.0"
description = "Reusable constraint types to use with typing.Annotated"
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev"]
files = [
{file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
{file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
@@ -199,6 +243,7 @@ version = "4.5.2"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
{file = "anyio-4.5.2-py3-none-any.whl", hash = "sha256:c011ee36bc1e8ba40e5a81cb9df91925c218fe9b778554e0b56a21e1b5d4716f"},
{file = "anyio-4.5.2.tar.gz", hash = "sha256:23009af4ed04ce05991845451e11ef02fc7c5ed29179ac9a420e5ad0ac7ddc5b"},
@@ -212,7 +257,7 @@ typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
[package.extras]
doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21.0b1) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\""]
trio = ["trio (>=0.26.1)"]
[[package]]
@@ -221,6 +266,8 @@ version = "0.1.4"
description = "Disable App Nap on macOS >= 10.9"
optional = false
python-versions = ">=3.6"
+groups = ["dev", "jupyter"]
+markers = "platform_system == \"Darwin\" or sys_platform == \"darwin\""
files = [
{file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
{file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
@@ -232,6 +279,7 @@ version = "25.1.0"
description = "Argon2 for Python"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
{file = "argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741"},
{file = "argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1"},
@@ -246,6 +294,7 @@ version = "21.2.0"
description = "Low-level CFFI bindings for Argon2"
optional = false
python-versions = ">=3.6"
+groups = ["dev", "jupyter"]
files = [
{file = "argon2-cffi-bindings-21.2.0.tar.gz", hash = "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3"},
{file = "argon2_cffi_bindings-21.2.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ccb949252cb2ab3a08c02024acb77cfb179492d5701c7cbdbfd776124d4d2367"},
@@ -283,6 +332,7 @@ version = "1.3.0"
description = "Better dates & times for Python"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
{file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"},
{file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"},
@@ -302,6 +352,7 @@ version = "3.0.0"
description = "Annotate AST trees with source code positions"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
{file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"},
{file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"},
@@ -317,6 +368,7 @@ version = "2.0.4"
description = "Simple LRU cache for asyncio"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
{file = "async-lru-2.0.4.tar.gz", hash = "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627"},
{file = "async_lru-2.0.4-py3-none-any.whl", hash = "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224"},
@@ -331,6 +383,8 @@ version = "5.0.1"
description = "Timeout context manager for asyncio programs"
optional = false
python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version < \"3.11\""
files = [
{file = "async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c"},
{file = "async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"},
@@ -342,18 +396,79 @@ version = "25.3.0"
description = "Classes Without Boilerplate"
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
{file = "attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3"},
{file = "attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b"},
]
[package.extras]
-benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+benchmark = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
+cov = ["cloudpickle ; platform_python_implementation == \"CPython\"", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
+dev = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"]
-tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
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name = "babel"
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description = "Internationalization utilities"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "docs", "jupyter"]
files = [
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[[package]]
name = "backcall"
@@ -378,6 +494,7 @@ version = "0.2.0"
description = "Specifications for callback functions passed in to an API"
optional = false
python-versions = "*"
+groups = ["dev", "jupyter"]
files = [
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@@ -389,6 +506,7 @@ version = "0.14.1"
description = "Unbearably fast runtime type checking in pure Python."
optional = false
python-versions = ">=3.7.0"
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test-tox-coverage = ["coverage (>=5.5)"]
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description = "Screen-scraping library"
optional = false
python-versions = ">=3.7.0"
+groups = ["dev", "docs", "jupyter"]
files = [
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@@ -429,6 +548,7 @@ version = "0.0.3"
description = "Python ABC plus abstract attributes"
optional = false
python-versions = "*"
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@@ -440,6 +560,7 @@ version = "23.12.1"
description = "The uncompromising code formatter."
optional = false
python-versions = ">=3.8"
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files = [
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[package.extras]
colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
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description = "An easy safelist-based HTML-sanitizing tool."
optional = false
python-versions = ">=3.8"
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[package.extras]
css = ["tinycss2 (>=1.1.0,<1.3)"]
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+groups = ["dev"]
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name = "coverage"
version = "7.6.1"
description = "Code coverage measurement for Python"
optional = false
python-versions = ">=3.8"
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[package.extras]
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name = "datasets"
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optional = false
python-versions = ">=3.8.0"
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description = "An implementation of the Debug Adapter Protocol for Python"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
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@@ -922,6 +1343,7 @@ version = "5.2.1"
description = "Decorators for Humans"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
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@@ -933,6 +1355,7 @@ version = "0.7.1"
description = "XML bomb protection for Python stdlib modules"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["dev", "docs", "jupyter"]
files = [
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@@ -944,6 +1367,7 @@ version = "0.3.8"
description = "serialize all of Python"
optional = false
python-versions = ">=3.8"
+groups = ["main"]
files = [
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@@ -959,6 +1383,7 @@ version = "0.20.1"
description = "Docutils -- Python Documentation Utilities"
optional = false
python-versions = ">=3.7"
+groups = ["docs"]
files = [
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@@ -970,6 +1395,7 @@ version = "0.8.1"
description = "A new flavour of deep learning operations"
optional = false
python-versions = ">=3.8"
+groups = ["main"]
files = [
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@@ -981,6 +1407,8 @@ version = "0.2.2"
description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
optional = false
python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version < \"3.10\""
files = [
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@@ -995,6 +1423,8 @@ version = "1.3.0"
description = "Backport of PEP 654 (exception groups)"
optional = false
python-versions = ">=3.7"
+groups = ["dev", "jupyter"]
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files = [
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@@ -1012,13 +1442,14 @@ version = "2.2.0"
description = "Get the currently executing AST node of a frame, and other information"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
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[[package]]
name = "fancy-einsum"
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description = "Drop-in replacement for torch/numpy einsum, with descriptive variable names in equations"
optional = false
python-versions = ">=3.6"
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files = [
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]
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+groups = ["dev"]
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+
[[package]]
name = "fastjsonschema"
version = "2.21.1"
description = "Fastest Python implementation of JSON schema"
optional = false
python-versions = "*"
+groups = ["dev", "docs", "jupyter"]
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devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
+[[package]]
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+groups = ["dev"]
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+groups = ["dev"]
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version = "3.16.1"
description = "A platform independent file lock."
optional = false
python-versions = ">=3.8"
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+typing = ["typing-extensions (>=4.12.2) ; python_version < \"3.11\""]
+
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+groups = ["dev"]
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python-versions = ">=3.8"
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python-versions = ">=3.8"
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optional = false
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optional = false
python-versions = ">=3.7"
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-test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
+test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock ; python_version < \"3.8\"", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions ; python_version < \"3.11\""]
+
+[[package]]
+name = "gradio"
+version = "4.44.1"
+description = "Python library for easily interacting with trained machine learning models"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version < \"3.10\""
+files = [
+ {file = "gradio-4.44.1-py3-none-any.whl", hash = "sha256:c908850c638e4a176b22f95a758ce6a63ffbc2a7a5a74b23186ceeeedc23f4d9"},
+ {file = "gradio-4.44.1.tar.gz", hash = "sha256:a68a52498ac6b63f8864ef84bf7866a70e7d07ebe913edf921e1d2a3708ad5ae"},
+]
+
+[package.dependencies]
+aiofiles = ">=22.0,<24.0"
+anyio = ">=3.0,<5.0"
+fastapi = "<1.0"
+ffmpy = "*"
+gradio-client = "1.3.0"
+httpx = ">=0.24.1"
+huggingface-hub = ">=0.19.3"
+importlib-resources = ">=1.3,<7.0"
+jinja2 = "<4.0"
+markupsafe = ">=2.0,<3.0"
+matplotlib = ">=3.0,<4.0"
+numpy = ">=1.0,<3.0"
+orjson = ">=3.0,<4.0"
+packaging = "*"
+pandas = ">=1.0,<3.0"
+pillow = ">=8.0,<11.0"
+pydantic = ">=2.0"
+pydub = "*"
+python-multipart = ">=0.0.9"
+pyyaml = ">=5.0,<7.0"
+ruff = {version = ">=0.2.2", markers = "sys_platform != \"emscripten\""}
+semantic-version = ">=2.0,<3.0"
+tomlkit = "0.12.0"
+typer = {version = ">=0.12,<1.0", markers = "sys_platform != \"emscripten\""}
+typing-extensions = ">=4.0,<5.0"
+urllib3 = ">=2.0,<3.0"
+uvicorn = {version = ">=0.14.0", markers = "sys_platform != \"emscripten\""}
+
+[package.extras]
+oauth = ["authlib", "itsdangerous"]
+
+[[package]]
+name = "gradio"
+version = "5.38.2"
+description = "Python library for easily interacting with trained machine learning models"
+optional = false
+python-versions = ">=3.10"
+groups = ["dev"]
+markers = "python_version >= \"3.10\""
+files = [
+ {file = "gradio-5.38.2-py3-none-any.whl", hash = "sha256:ef2a1099843868296881a89bfe5dbd71a6f72530a1d82512cd82cfe48bc05b32"},
+ {file = "gradio-5.38.2.tar.gz", hash = "sha256:34c49aa6c038ea5b21a1184ea94c5db2fe52bdfff6ecd3a22d2b913034d1ba4d"},
+]
+
+[package.dependencies]
+aiofiles = ">=22.0,<25.0"
+anyio = ">=3.0,<5.0"
+audioop-lts = {version = "<1.0", markers = "python_version >= \"3.13\""}
+brotli = ">=1.1.0"
+fastapi = ">=0.115.2,<1.0"
+ffmpy = "*"
+gradio-client = "1.11.0"
+groovy = ">=0.1,<1.0"
+httpx = ">=0.24.1,<1.0"
+huggingface-hub = ">=0.28.1"
+jinja2 = "<4.0"
+markupsafe = ">=2.0,<4.0"
+numpy = ">=1.0,<3.0"
+orjson = ">=3.0,<4.0"
+packaging = "*"
+pandas = ">=1.0,<3.0"
+pillow = ">=8.0,<12.0"
+pydantic = ">=2.0,<2.12"
+pydub = "*"
+python-multipart = ">=0.0.18"
+pyyaml = ">=5.0,<7.0"
+ruff = {version = ">=0.9.3", markers = "sys_platform != \"emscripten\""}
+safehttpx = ">=0.1.6,<0.2.0"
+semantic-version = ">=2.0,<3.0"
+starlette = {version = ">=0.40.0,<1.0", markers = "sys_platform != \"emscripten\""}
+tomlkit = ">=0.12.0,<0.14.0"
+typer = {version = ">=0.12,<1.0", markers = "sys_platform != \"emscripten\""}
+typing-extensions = ">=4.0,<5.0"
+urllib3 = {version = ">=2.0,<3.0", markers = "sys_platform == \"emscripten\""}
+uvicorn = {version = ">=0.14.0", markers = "sys_platform != \"emscripten\""}
+
+[package.extras]
+mcp = ["mcp (==1.10.1)", "pydantic (>=2.11) ; sys_platform != \"emscripten\""]
+oauth = ["authlib", "itsdangerous"]
+
+[[package]]
+name = "gradio-client"
+version = "1.3.0"
+description = "Python library for easily interacting with trained machine learning models"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version < \"3.10\""
+files = [
+ {file = "gradio_client-1.3.0-py3-none-any.whl", hash = "sha256:20c40cb4d56e18de1a025ccf58079f08a304e4fb2dfbcf7c2352815b2cb31091"},
+ {file = "gradio_client-1.3.0.tar.gz", hash = "sha256:d904afeae4f5682add0a6a263542c10e7669ff6c9de0a53a5c2fc9b719a24bb8"},
+]
+
+[package.dependencies]
+fsspec = "*"
+httpx = ">=0.24.1"
+huggingface-hub = ">=0.19.3"
+packaging = "*"
+typing-extensions = ">=4.0,<5.0"
+websockets = ">=10.0,<13.0"
+
+[[package]]
+name = "gradio-client"
+version = "1.11.0"
+description = "Python library for easily interacting with trained machine learning models"
+optional = false
+python-versions = ">=3.10"
+groups = ["dev"]
+markers = "python_version >= \"3.10\""
+files = [
+ {file = "gradio_client-1.11.0-py3-none-any.whl", hash = "sha256:afb714aea50224f6f04679fe2ce79c1be75011012d0dc3b3ee575610a0dc8eb2"},
+ {file = "gradio_client-1.11.0.tar.gz", hash = "sha256:377c31d8082173663b230dad341614b127b2460fe24d5fd72ed456fb3f0b3a9e"},
+]
+
+[package.dependencies]
+fsspec = "*"
+httpx = ">=0.24.1"
+huggingface-hub = ">=0.19.3"
+packaging = "*"
+typing-extensions = ">=4.0,<5.0"
+websockets = ">=10.0,<16.0"
+
+[[package]]
+name = "groovy"
+version = "0.1.2"
+description = "A small Python library created to help developers protect their applications from Server Side Request Forgery (SSRF) attacks."
+optional = false
+python-versions = ">3.9"
+groups = ["dev"]
+markers = "python_version >= \"3.10\""
+files = [
+ {file = "groovy-0.1.2-py3-none-any.whl", hash = "sha256:7f7975bab18c729a257a8b1ae9dcd70b7cafb1720481beae47719af57c35fa64"},
+ {file = "groovy-0.1.2.tar.gz", hash = "sha256:25c1dc09b3f9d7e292458aa762c6beb96ea037071bf5e917fc81fb78d2231083"},
+]
+
+[package.extras]
+dev = ["pytest", "ruff (==0.9.3)"]
[[package]]
name = "h11"
@@ -1270,6 +2071,7 @@ version = "0.16.0"
description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
{file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"},
{file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
@@ -1281,6 +2083,8 @@ version = "1.1.5"
description = "Fast transfer of large files with the Hugging Face Hub."
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""
files = [
{file = "hf_xet-1.1.5-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:f52c2fa3635b8c37c7764d8796dfa72706cc4eded19d638331161e82b0792e23"},
{file = "hf_xet-1.1.5-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:9fa6e3ee5d61912c4a113e0708eaaef987047616465ac7aa30f7121a48fc1af8"},
@@ -1301,6 +2105,7 @@ version = "1.0.9"
description = "A minimal low-level HTTP client."
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
{file = "httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55"},
{file = "httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8"},
@@ -1322,6 +2127,7 @@ version = "0.28.1"
description = "The next generation HTTP client."
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
{file = "httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad"},
{file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"},
@@ -1334,7 +2140,7 @@ httpcore = "==1.*"
idna = "*"
[package.extras]
-brotli = ["brotli", "brotlicffi"]
+brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
http2 = ["h2 (>=3,<5)"]
socks = ["socksio (==1.*)"]
@@ -1346,6 +2152,7 @@ version = "0.33.0"
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
optional = false
python-versions = ">=3.8.0"
+groups = ["main", "dev"]
files = [
{file = "huggingface_hub-0.33.0-py3-none-any.whl", hash = "sha256:e8668875b40c68f9929150d99727d39e5ebb8a05a98e4191b908dc7ded9074b3"},
{file = "huggingface_hub-0.33.0.tar.gz", hash = "sha256:aa31f70d29439d00ff7a33837c03f1f9dd83971ce4e29ad664d63ffb17d3bb97"},
@@ -1362,16 +2169,16 @@ tqdm = ">=4.42.1"
typing-extensions = ">=3.7.4.3"
[package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (==1.4.0)", "mypy (==1.15.0)", "mypy (>=1.14.1,<1.15.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (==1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (==1.4.0)", "mypy (==1.15.0)", "mypy (>=1.14.1,<1.15.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (==1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
hf-transfer = ["hf-transfer (>=0.1.4)"]
hf-xet = ["hf-xet (>=1.1.2,<2.0.0)"]
inference = ["aiohttp"]
mcp = ["aiohttp", "mcp (>=1.8.0)", "typer"]
oauth = ["authlib (>=1.3.2)", "fastapi", "httpx", "itsdangerous"]
-quality = ["libcst (==1.4.0)", "mypy (==1.15.0)", "mypy (>=1.14.1,<1.15.0)", "ruff (>=0.9.0)"]
+quality = ["libcst (==1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "ruff (>=0.9.0)"]
tensorflow = ["graphviz", "pydot", "tensorflow"]
tensorflow-testing = ["keras (<3.0)", "tensorflow"]
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
@@ -1384,6 +2191,7 @@ version = "3.10"
description = "Internationalized Domain Names in Applications (IDNA)"
optional = false
python-versions = ">=3.6"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
{file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
{file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
@@ -1398,6 +2206,7 @@ version = "1.4.1"
description = "Getting image size from png/jpeg/jpeg2000/gif file"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["docs"]
files = [
{file = "imagesize-1.4.1-py2.py3-none-any.whl", hash = "sha256:0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b"},
{file = "imagesize-1.4.1.tar.gz", hash = "sha256:69150444affb9cb0d5cc5a92b3676f0b2fb7cd9ae39e947a5e11a36b4497cd4a"},
@@ -1409,21 +2218,23 @@ version = "8.5.0"
description = "Read metadata from Python packages"
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
{file = "importlib_metadata-8.5.0-py3-none-any.whl", hash = "sha256:45e54197d28b7a7f1559e60b95e7c567032b602131fbd588f1497f47880aa68b"},
{file = "importlib_metadata-8.5.0.tar.gz", hash = "sha256:71522656f0abace1d072b9e5481a48f07c138e00f079c38c8f883823f9c26bd7"},
]
+markers = {main = "python_version < \"3.10\"", docs = "python_version < \"3.10\"", jupyter = "python_version < \"3.10\""}
[package.dependencies]
zipp = ">=3.20"
[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
cover = ["pytest-cov"]
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
enabler = ["pytest-enabler (>=2.2)"]
perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+test = ["flufl.flake8", "importlib-resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
type = ["pytest-mypy"]
[[package]]
@@ -1432,16 +2243,18 @@ version = "6.4.5"
description = "Read resources from Python packages"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "docs", "jupyter"]
files = [
{file = "importlib_resources-6.4.5-py3-none-any.whl", hash = "sha256:ac29d5f956f01d5e4bb63102a5a19957f1b9175e45649977264a1416783bb717"},
{file = "importlib_resources-6.4.5.tar.gz", hash = "sha256:980862a1d16c9e147a59603677fa2aa5fd82b87f223b6cb870695bcfce830065"},
]
+markers = {dev = "python_version < \"3.10\"", docs = "python_version == \"3.8\"", jupyter = "python_version == \"3.8\""}
[package.dependencies]
zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
cover = ["pytest-cov"]
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
enabler = ["pytest-enabler (>=2.2)"]
@@ -1454,6 +2267,7 @@ version = "2.1.0"
description = "brain-dead simple config-ini parsing"
optional = false
python-versions = ">=3.8"
+groups = ["dev"]
files = [
{file = "iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760"},
{file = "iniconfig-2.1.0.tar.gz", hash = "sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7"},
@@ -1465,6 +2279,7 @@ version = "6.29.5"
description = "IPython Kernel for Jupyter"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
{file = "ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5"},
{file = "ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215"},
@@ -1498,6 +2313,7 @@ version = "8.12.3"
description = "IPython: Productive Interactive Computing"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
{file = "ipython-8.12.3-py3-none-any.whl", hash = "sha256:b0340d46a933d27c657b211a329d0be23793c36595acf9e6ef4164bc01a1804c"},
{file = "ipython-8.12.3.tar.gz", hash = "sha256:3910c4b54543c2ad73d06579aa771041b7d5707b033bd488669b4cf544e3b363"},
@@ -1537,6 +2353,7 @@ version = "8.1.7"
description = "Jupyter interactive widgets"
optional = false
python-versions = ">=3.7"
+groups = ["dev", "jupyter"]
files = [
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description = "Operations with ISO 8601 durations"
optional = false
python-versions = ">=3.7"
+groups = ["dev", "jupyter"]
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@@ -1572,6 +2390,7 @@ version = "5.8.0"
description = "A Python utility / library to sort Python imports."
optional = false
python-versions = ">=3.6,<4.0"
+groups = ["dev"]
files = [
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description = "Type annotations and runtime checking for shape and dtype of JAX arrays, and PyTrees."
optional = false
python-versions = "~=3.8"
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@@ -1604,6 +2424,7 @@ version = "0.19.2"
description = "An autocompletion tool for Python that can be used for text editors."
optional = false
python-versions = ">=3.6"
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description = "A very fast and expressive template engine."
optional = false
python-versions = ">=3.7"
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files = [
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description = "A Python implementation of the JSON5 data format."
optional = false
python-versions = ">=3.8.0"
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[[package]]
name = "jsonpointer"
@@ -1654,6 +2477,7 @@ version = "3.0.0"
description = "Identify specific nodes in a JSON document (RFC 6901)"
optional = false
python-versions = ">=3.7"
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description = "An implementation of JSON Schema validation for Python"
optional = false
python-versions = ">=3.8"
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description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
optional = false
python-versions = ">=3.8"
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description = "Jupyter metapackage. Install all the Jupyter components in one go."
optional = false
python-versions = "*"
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description = "Jupyter protocol implementation and client libraries"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "docs", "jupyter"]
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@@ -1753,6 +2581,7 @@ version = "6.6.3"
description = "Jupyter terminal console"
optional = false
python-versions = ">=3.7"
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optional = false
python-versions = ">=3.8"
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optional = false
python-versions = ">=3.8"
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optional = false
python-versions = ">=3.8"
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python-versions = ">=3.8"
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optional = false
python-versions = ">=3.8"
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optional = false
python-versions = ">=3.8"
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optional = false
python-versions = ">=3.8"
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optional = false
python-versions = ">=3.8"
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python-versions = ">=3.7"
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+[package.dependencies]
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+cycler = ">=0.10"
+fonttools = ">=4.22.0"
+importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
+kiwisolver = ">=1.3.1"
+numpy = ">=1.23"
+packaging = ">=20.0"
+pillow = ">=8"
+pyparsing = ">=2.3.1"
+python-dateutil = ">=2.7"
+
+[package.extras]
+dev = ["meson-python (>=0.13.1,<0.17.0)", "numpy (>=1.25)", "pybind11 (>=2.6,!=2.13.3)", "setuptools (>=64)", "setuptools_scm (>=7)"]
+
[[package]]
name = "matplotlib-inline"
version = "0.1.7"
description = "Inline Matplotlib backend for Jupyter"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
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@@ -2145,6 +3250,7 @@ version = "0.4.2"
description = "Collection of plugins for markdown-it-py"
optional = false
python-versions = ">=3.8"
+groups = ["docs"]
files = [
{file = "mdit_py_plugins-0.4.2-py3-none-any.whl", hash = "sha256:0c673c3f889399a33b95e88d2f0d111b4447bdfea7f237dab2d488f459835636"},
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@@ -2164,6 +3270,7 @@ version = "0.1.2"
description = "Markdown URL utilities"
optional = false
python-versions = ">=3.7"
+groups = ["main", "dev", "docs"]
files = [
{file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"},
{file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
@@ -2175,6 +3282,7 @@ version = "3.1.3"
description = "A sane and fast Markdown parser with useful plugins and renderers"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "docs", "jupyter"]
files = [
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@@ -2189,6 +3297,7 @@ version = "1.3.0"
description = "Python library for arbitrary-precision floating-point arithmetic"
optional = false
python-versions = "*"
+groups = ["main", "dev"]
files = [
{file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
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@@ -2197,7 +3306,7 @@ files = [
[package.extras]
develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
docs = ["sphinx"]
-gmpy = ["gmpy2 (>=2.1.0a4)"]
+gmpy = ["gmpy2 (>=2.1.0a4) ; platform_python_implementation != \"PyPy\""]
tests = ["pytest (>=4.6)"]
[[package]]
@@ -2206,6 +3315,7 @@ version = "6.1.0"
description = "multidict implementation"
optional = false
python-versions = ">=3.8"
+groups = ["main"]
files = [
{file = "multidict-6.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3380252550e372e8511d49481bd836264c009adb826b23fefcc5dd3c69692f60"},
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@@ -2310,6 +3420,7 @@ version = "0.70.16"
description = "better multiprocessing and multithreading in Python"
optional = false
python-versions = ">=3.8"
+groups = ["main"]
files = [
{file = "multiprocess-0.70.16-pp310-pypy310_pp73-macosx_10_13_x86_64.whl", hash = "sha256:476887be10e2f59ff183c006af746cb6f1fd0eadcfd4ef49e605cbe2659920ee"},
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@@ -2334,18 +3445,19 @@ version = "0.6.21"
description = "miscellaneous python utilities"
optional = false
python-versions = ">=3.8"
+groups = ["docs"]
files = [
{file = "muutils-0.6.21-py3-none-any.whl", hash = "sha256:6f2fbd483890d41131ede2f9dcf396e9b874e227c5917342a738b37c56f4b689"},
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-array-no-torch = ["jaxtyping (>=0.2.12)", "numpy (>1.24.4)", "numpy (>=1.24.4)"]
+array = ["jaxtyping (>=0.2.12)", "numpy (>1.24.4) ; python_version >= \"3.9\"", "numpy (>=1.24.4) ; python_version < \"3.9\"", "torch (>=1.13.1) ; python_version >= \"3.9\" and python_version < \"3.13\"", "torch (>=1.13.1,<2.5.0) ; python_version < \"3.9\"", "torch (>=2.5.0) ; python_version >= \"3.13\""]
+array-nb-zanj = ["ipython (>=8.0.0)", "jaxtyping (>=0.2.12)", "numpy (>1.24.4) ; python_version >= \"3.9\"", "numpy (>=1.24.4) ; python_version < \"3.9\"", "torch (>=1.13.1) ; python_version >= \"3.9\" and python_version < \"3.13\"", "torch (>=1.13.1,<2.5.0) ; python_version < \"3.9\"", "torch (>=2.5.0) ; python_version >= \"3.13\"", "zanj (>=0.3.0) ; python_version >= \"3.10\""]
+array-no-torch = ["jaxtyping (>=0.2.12)", "numpy (>1.24.4) ; python_version >= \"3.9\"", "numpy (>=1.24.4) ; python_version < \"3.9\""]
notebook = ["ipython (>=8.0.0)"]
parallel = ["multiprocess (>=0.70.17)", "tqdm (>=4.67.1)"]
-zanj = ["zanj (>=0.3.0)"]
+zanj = ["zanj (>=0.3.0) ; python_version >= \"3.10\""]
[[package]]
name = "mypy"
@@ -2353,6 +3465,7 @@ version = "1.14.1"
description = "Optional static typing for Python"
optional = false
python-versions = ">=3.8"
+groups = ["dev"]
files = [
{file = "mypy-1.14.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:52686e37cf13d559f668aa398dd7ddf1f92c5d613e4f8cb262be2fb4fedb0fcb"},
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@@ -2412,6 +3525,7 @@ version = "1.1.0"
description = "Type system extensions for programs checked with the mypy type checker."
optional = false
python-versions = ">=3.8"
+groups = ["dev"]
files = [
{file = "mypy_extensions-1.1.0-py3-none-any.whl", hash = "sha256:1be4cccdb0f2482337c4743e60421de3a356cd97508abadd57d47403e94f5505"},
{file = "mypy_extensions-1.1.0.tar.gz", hash = "sha256:52e68efc3284861e772bbcd66823fde5ae21fd2fdb51c62a211403730b916558"},
@@ -2423,6 +3537,7 @@ version = "2.0.0"
description = "An extended [CommonMark](https://spec.commonmark.org/) compliant parser,"
optional = false
python-versions = ">=3.8"
+groups = ["docs"]
files = [
{file = "myst_parser-2.0.0-py3-none-any.whl", hash = "sha256:7c36344ae39c8e740dad7fdabf5aa6fc4897a813083c6cc9990044eb93656b14"},
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@@ -2449,6 +3564,7 @@ version = "1.42.1"
description = "Extremely lightweight compatibility layer between dataframe libraries"
optional = false
python-versions = ">=3.8"
+groups = ["dev"]
files = [
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@@ -2473,6 +3589,7 @@ version = "0.10.1"
description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
optional = false
python-versions = ">=3.8.0"
+groups = ["dev", "docs", "jupyter"]
files = [
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@@ -2495,6 +3612,7 @@ version = "7.16.6"
description = "Converting Jupyter Notebooks (.ipynb files) to other formats. Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script. nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
optional = false
python-versions = ">=3.8"
+groups = ["dev", "docs", "jupyter"]
files = [
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@@ -2532,6 +3650,7 @@ version = "5.10.4"
description = "The Jupyter Notebook format"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "docs", "jupyter"]
files = [
{file = "nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b"},
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@@ -2553,6 +3672,7 @@ version = "0.9.7"
description = "Jupyter Notebook Tools for Sphinx"
optional = false
python-versions = ">=3.6"
+groups = ["docs"]
files = [
{file = "nbsphinx-0.9.7-py3-none-any.whl", hash = "sha256:7292c3767fea29e405c60743eee5393682a83982ab202ff98f5eb2db02629da8"},
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@@ -2572,6 +3692,7 @@ version = "0.10.0"
description = "A py.test plugin to validate Jupyter notebooks"
optional = false
python-versions = ">=3.6, <4"
+groups = ["dev"]
files = [
{file = "nbval-0.10.0-py2.py3-none-any.whl", hash = "sha256:427e42caabeae39f493d8baca629b03816269fc11f1b7e2046e10929a3149a73"},
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@@ -2590,6 +3711,7 @@ version = "1.6.0"
description = "Patch asyncio to allow nested event loops"
optional = false
python-versions = ">=3.5"
+groups = ["dev", "jupyter"]
files = [
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@@ -2601,6 +3723,8 @@ version = "3.1"
description = "Python package for creating and manipulating graphs and networks"
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version == \"3.8\""
files = [
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{file = "networkx-3.1.tar.gz", hash = "sha256:de346335408f84de0eada6ff9fafafff9bcda11f0a0dfaa931133debb146ab61"},
@@ -2619,6 +3743,8 @@ version = "3.2.1"
description = "Python package for creating and manipulating graphs and networks"
optional = false
python-versions = ">=3.9"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.9\""
files = [
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@@ -2637,6 +3763,7 @@ version = "7.3.3"
description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
optional = false
python-versions = ">=3.8"
+groups = ["dev"]
files = [
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@@ -2652,7 +3779,7 @@ tornado = ">=6.2.0"
[package.extras]
dev = ["hatch", "pre-commit"]
docs = ["myst-parser", "nbsphinx", "pydata-sphinx-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
-test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.27.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]
+test = ["importlib-resources (>=5.0) ; python_version < \"3.10\"", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.27.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]
[[package]]
name = "notebook-shim"
@@ -2660,6 +3787,7 @@ version = "0.2.4"
description = "A shim layer for notebook traits and config"
optional = false
python-versions = ">=3.7"
+groups = ["dev", "jupyter"]
files = [
{file = "notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef"},
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@@ -2677,6 +3805,8 @@ version = "1.24.4"
description = "Fundamental package for array computing in Python"
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version == \"3.8\""
files = [
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@@ -2714,6 +3844,8 @@ version = "1.26.4"
description = "Fundamental package for array computing in Python"
optional = false
python-versions = ">=3.9"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.9\""
files = [
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@@ -2759,6 +3891,8 @@ version = "12.4.5.8"
description = "CUBLAS native runtime libraries"
optional = false
python-versions = ">=3"
+groups = ["main", "dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version == \"3.8\""
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version = "7.7.0"
description = "A decorator to automatically detect mismatch when overriding a method."
optional = false
python-versions = ">=3.6"
+groups = ["dev", "jupyter"]
files = [
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@@ -3119,6 +4492,7 @@ version = "25.0"
description = "Core utilities for Python packages"
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
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description = "Powerful data structures for data analysis, time series, and statistics"
optional = false
python-versions = ">=3.8"
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{version = ">=1.23.2", markers = "python_version >= \"3.11\""},
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python-dateutil = ">=2.8.2"
pytz = ">=2020.1"
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description = "Pandoc Documents for Python"
optional = false
python-versions = "*"
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description = "Utilities for writing pandoc filters in python"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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description = "A Python Parser"
optional = false
python-versions = ">=3.6"
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description = "Utility library for gitignore style pattern matching of file paths."
optional = false
python-versions = ">=3.8"
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description = "Pexpect allows easy control of interactive console applications."
optional = false
python-versions = "*"
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optional = false
python-versions = "*"
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+description = "Python Imaging Library (Fork)"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev"]
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+]
+
+[package.extras]
+docs = ["furo", "olefile", "sphinx (>=8.2)", "sphinx-autobuild", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
+fpx = ["olefile"]
+mic = ["olefile"]
+test-arrow = ["pyarrow"]
+tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "trove-classifiers (>=2024.10.12)"]
+typing = ["typing-extensions ; python_version < \"3.10\""]
+xmp = ["defusedxml"]
+
[[package]]
name = "pkgutil-resolve-name"
version = "1.3.10"
description = "Resolve a name to an object."
optional = false
python-versions = ">=3.6"
+groups = ["dev", "docs", "jupyter"]
+markers = "python_version == \"3.8\""
files = [
{file = "pkgutil_resolve_name-1.3.10-py3-none-any.whl", hash = "sha256:ca27cc078d25c5ad71a9de0a7a330146c4e014c2462d9af19c6b828280649c5e"},
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@@ -3284,6 +4893,7 @@ version = "4.3.6"
description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
{file = "platformdirs-4.3.6-py3-none-any.whl", hash = "sha256:73e575e1408ab8103900836b97580d5307456908a03e92031bab39e4554cc3fb"},
{file = "platformdirs-4.3.6.tar.gz", hash = "sha256:357fb2acbc885b0419afd3ce3ed34564c13c9b95c89360cd9563f73aa5e2b907"},
@@ -3300,6 +4910,7 @@ version = "6.1.2"
description = "An open-source interactive data visualization library for Python"
optional = false
python-versions = ">=3.8"
+groups = ["dev"]
files = [
{file = "plotly-6.1.2-py3-none-any.whl", hash = "sha256:f1548a8ed9158d59e03d7fed548c7db5549f3130d9ae19293c8638c202648f6d"},
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@@ -3320,6 +4931,7 @@ version = "1.5.0"
description = "plugin and hook calling mechanisms for python"
optional = false
python-versions = ">=3.8"
+groups = ["dev"]
files = [
{file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"},
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@@ -3335,6 +4947,7 @@ version = "1.9.0"
description = "Plumbum: shell combinators library"
optional = false
python-versions = ">=3.8"
+groups = ["docs"]
files = [
{file = "plumbum-1.9.0-py3-none-any.whl", hash = "sha256:9fd0d3b0e8d86e4b581af36edf3f3bbe9d1ae15b45b8caab28de1bcb27aaa7f5"},
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@@ -3356,6 +4969,7 @@ version = "3.11"
description = "Python Lex & Yacc"
optional = false
python-versions = "*"
+groups = ["docs"]
files = [
{file = "ply-3.11-py2.py3-none-any.whl", hash = "sha256:096f9b8350b65ebd2fd1346b12452efe5b9607f7482813ffca50c22722a807ce"},
{file = "ply-3.11.tar.gz", hash = "sha256:00c7c1aaa88358b9c765b6d3000c6eec0ba42abca5351b095321aef446081da3"},
@@ -3367,6 +4981,7 @@ version = "0.9.1"
description = "A collection of helpful Python tools!"
optional = false
python-versions = "*"
+groups = ["docs"]
files = [
{file = "pockets-0.9.1-py2.py3-none-any.whl", hash = "sha256:68597934193c08a08eb2bf6a1d85593f627c22f9b065cc727a4f03f669d96d86"},
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@@ -3381,6 +4996,7 @@ version = "0.21.1"
description = "Python client for the Prometheus monitoring system."
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
{file = "prometheus_client-0.21.1-py3-none-any.whl", hash = "sha256:594b45c410d6f4f8888940fe80b5cc2521b305a1fafe1c58609ef715a001f301"},
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@@ -3395,6 +5011,7 @@ version = "3.0.51"
description = "Library for building powerful interactive command lines in Python"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
{file = "prompt_toolkit-3.0.51-py3-none-any.whl", hash = "sha256:52742911fde84e2d423e2f9a4cf1de7d7ac4e51958f648d9540e0fb8db077b07"},
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@@ -3409,6 +5026,7 @@ version = "0.2.0"
description = "Accelerated property cache"
optional = false
python-versions = ">=3.8"
+groups = ["main"]
files = [
{file = "propcache-0.2.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:c5869b8fd70b81835a6f187c5fdbe67917a04d7e52b6e7cc4e5fe39d55c39d58"},
{file = "propcache-0.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:952e0d9d07609d9c5be361f33b0d6d650cd2bae393aabb11d9b719364521984b"},
@@ -3516,6 +5134,8 @@ version = "5.29.5"
description = ""
optional = false
python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version == \"3.8\""
files = [
{file = "protobuf-5.29.5-cp310-abi3-win32.whl", hash = "sha256:3f1c6468a2cfd102ff4703976138844f78ebd1fb45f49011afc5139e9e283079"},
{file = "protobuf-5.29.5-cp310-abi3-win_amd64.whl", hash = "sha256:3f76e3a3675b4a4d867b52e4a5f5b78a2ef9565549d4037e06cf7b0942b1d3fc"},
@@ -3536,6 +5156,8 @@ version = "6.31.1"
description = ""
optional = false
python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version >= \"3.9\""
files = [
{file = "protobuf-6.31.1-cp310-abi3-win32.whl", hash = "sha256:7fa17d5a29c2e04b7d90e5e32388b8bfd0e7107cd8e616feef7ed3fa6bdab5c9"},
{file = "protobuf-6.31.1-cp310-abi3-win_amd64.whl", hash = "sha256:426f59d2964864a1a366254fa703b8632dcec0790d8862d30034d8245e1cd447"},
@@ -3554,6 +5176,7 @@ version = "7.0.0"
description = "Cross-platform lib for process and system monitoring in Python. NOTE: the syntax of this script MUST be kept compatible with Python 2.7."
optional = false
python-versions = ">=3.6"
+groups = ["main", "dev", "jupyter"]
files = [
{file = "psutil-7.0.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:101d71dc322e3cffd7cea0650b09b3d08b8e7c4109dd6809fe452dfd00e58b25"},
{file = "psutil-7.0.0-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:39db632f6bb862eeccf56660871433e111b6ea58f2caea825571951d4b6aa3da"},
@@ -3577,6 +5200,8 @@ version = "0.7.0"
description = "Run a subprocess in a pseudo terminal"
optional = false
python-versions = "*"
+groups = ["dev", "jupyter"]
+markers = "os_name != \"nt\" or sys_platform != \"win32\""
files = [
{file = "ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35"},
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@@ -3588,6 +5213,7 @@ version = "0.2.3"
description = "Safely evaluate AST nodes without side effects"
optional = false
python-versions = "*"
+groups = ["dev", "jupyter"]
files = [
{file = "pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0"},
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@@ -3602,6 +5228,7 @@ version = "17.0.0"
description = "Python library for Apache Arrow"
optional = false
python-versions = ">=3.8"
+groups = ["main"]
files = [
{file = "pyarrow-17.0.0-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:a5c8b238d47e48812ee577ee20c9a2779e6a5904f1708ae240f53ecbee7c9f07"},
{file = "pyarrow-17.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:db023dc4c6cae1015de9e198d41250688383c3f9af8f565370ab2b4cb5f62655"},
@@ -3653,6 +5280,7 @@ version = "2.5.0"
description = "A formatter for finding and removing unused import statements."
optional = false
python-versions = "<4,>=3.8"
+groups = ["dev"]
files = [
{file = "pycln-2.5.0-py3-none-any.whl", hash = "sha256:6aec7a5b8df47e23399842b1f8470da4164956e26391f9b86c5edced5344da92"},
{file = "pycln-2.5.0.tar.gz", hash = "sha256:f3a64486f813cd29da07940c4c2bb412080a23b9b0df9b0b1576c8e39ac79c44"},
@@ -3671,10 +5299,12 @@ version = "2.22"
description = "C parser in Python"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "docs", "jupyter"]
files = [
{file = "pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc"},
{file = "pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6"},
]
+markers = {docs = "implementation_name == \"pypy\""}
[[package]]
name = "pydantic"
@@ -3682,6 +5312,7 @@ version = "2.10.6"
description = "Data validation using Python type hints"
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev"]
files = [
{file = "pydantic-2.10.6-py3-none-any.whl", hash = "sha256:427d664bf0b8a2b34ff5dd0f5a18df00591adcee7198fbd71981054cef37b584"},
{file = "pydantic-2.10.6.tar.gz", hash = "sha256:ca5daa827cce33de7a42be142548b0096bf05a7e7b365aebfa5f8eeec7128236"},
@@ -3694,7 +5325,7 @@ typing-extensions = ">=4.12.2"
[package.extras]
email = ["email-validator (>=2.0.0)"]
-timezone = ["tzdata"]
+timezone = ["tzdata ; python_version >= \"3.9\" and platform_system == \"Windows\""]
[[package]]
name = "pydantic-core"
@@ -3702,6 +5333,7 @@ version = "2.27.2"
description = "Core functionality for Pydantic validation and serialization"
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev"]
files = [
{file = "pydantic_core-2.27.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:2d367ca20b2f14095a8f4fa1210f5a7b78b8a20009ecced6b12818f455b1e9fa"},
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@@ -3808,12 +5440,25 @@ files = [
[package.dependencies]
typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
+[[package]]
+name = "pydub"
+version = "0.25.1"
+description = "Manipulate audio with an simple and easy high level interface"
+optional = false
+python-versions = "*"
+groups = ["dev"]
+files = [
+ {file = "pydub-0.25.1-py2.py3-none-any.whl", hash = "sha256:65617e33033874b59d87db603aa1ed450633288aefead953b30bded59cb599a6"},
+ {file = "pydub-0.25.1.tar.gz", hash = "sha256:980a33ce9949cab2a569606b65674d748ecbca4f0796887fd6f46173a7b0d30f"},
+]
+
[[package]]
name = "pygments"
version = "2.19.2"
description = "Pygments is a syntax highlighting package written in Python."
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
{file = "pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b"},
{file = "pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887"},
@@ -3822,12 +5467,45 @@ files = [
[package.extras]
windows-terminal = ["colorama (>=0.4.6)"]
+[[package]]
+name = "pyparsing"
+version = "3.1.4"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.6.8"
+groups = ["dev"]
+markers = "python_version == \"3.8\""
+files = [
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+ {file = "pyparsing-3.1.4.tar.gz", hash = "sha256:f86ec8d1a83f11977c9a6ea7598e8c27fc5cddfa5b07ea2241edbbde1d7bc032"},
+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
+[[package]]
+name = "pyparsing"
+version = "3.2.5"
+description = "pyparsing - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.9"
+groups = ["dev"]
+markers = "python_version == \"3.9\""
+files = [
+ {file = "pyparsing-3.2.5-py3-none-any.whl", hash = "sha256:e38a4f02064cf41fe6593d328d0512495ad1f3d8a91c4f73fc401b3079a59a5e"},
+ {file = "pyparsing-3.2.5.tar.gz", hash = "sha256:2df8d5b7b2802ef88e8d016a2eb9c7aeaa923529cd251ed0fe4608275d4105b6"},
+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
[[package]]
name = "pytest"
version = "8.3.5"
description = "pytest: simple powerful testing with Python"
optional = false
python-versions = ">=3.8"
+groups = ["dev"]
files = [
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@@ -3850,6 +5528,7 @@ version = "5.0.0"
description = "Pytest plugin for measuring coverage."
optional = false
python-versions = ">=3.8"
+groups = ["dev"]
files = [
{file = "pytest-cov-5.0.0.tar.gz", hash = "sha256:5837b58e9f6ebd335b0f8060eecce69b662415b16dc503883a02f45dfeb14857"},
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@@ -3868,6 +5547,7 @@ version = "1.3.0"
description = "Pytest plugin with advanced doctest features."
optional = false
python-versions = ">=3.8"
+groups = ["dev"]
files = [
{file = "pytest_doctestplus-1.3.0-py3-none-any.whl", hash = "sha256:4a7385d3e678881bb960e9200aa0db62ee32d575b3fa10d6735e8f1542c638f8"},
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@@ -3886,6 +5566,7 @@ version = "2.9.0.post0"
description = "Extensions to the standard Python datetime module"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
{file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
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@@ -3900,6 +5581,7 @@ version = "3.3.0"
description = "JSON Log Formatter for the Python Logging Package"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
{file = "python_json_logger-3.3.0-py3-none-any.whl", hash = "sha256:dd980fae8cffb24c13caf6e158d3d61c0d6d22342f932cb6e9deedab3d35eec7"},
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@@ -3909,7 +5591,19 @@ files = [
typing_extensions = {version = "*", markers = "python_version < \"3.10\""}
[package.extras]
-dev = ["backports.zoneinfo", "black", "build", "freezegun", "mdx_truly_sane_lists", "mike", "mkdocs", "mkdocs-awesome-pages-plugin", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-material (>=8.5)", "mkdocstrings[python]", "msgspec", "mypy", "orjson", "pylint", "pytest", "tzdata", "validate-pyproject[all]"]
+dev = ["backports.zoneinfo ; python_version < \"3.9\"", "black", "build", "freezegun", "mdx_truly_sane_lists", "mike", "mkdocs", "mkdocs-awesome-pages-plugin", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-material (>=8.5)", "mkdocstrings[python]", "msgspec ; implementation_name != \"pypy\"", "mypy", "orjson ; implementation_name != \"pypy\"", "pylint", "pytest", "tzdata", "validate-pyproject[all]"]
+
+[[package]]
+name = "python-multipart"
+version = "0.0.20"
+description = "A streaming multipart parser for Python"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev"]
+files = [
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+ {file = "python_multipart-0.0.20.tar.gz", hash = "sha256:8dd0cab45b8e23064ae09147625994d090fa46f5b0d1e13af944c331a7fa9d13"},
+]
[[package]]
name = "pytz"
@@ -3917,10 +5611,12 @@ version = "2025.2"
description = "World timezone definitions, modern and historical"
optional = false
python-versions = "*"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
{file = "pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00"},
{file = "pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3"},
]
+markers = {docs = "python_version == \"3.8\"", jupyter = "python_version == \"3.8\""}
[[package]]
name = "pywin32"
@@ -3928,6 +5624,7 @@ version = "310"
description = "Python for Window Extensions"
optional = false
python-versions = "*"
+groups = ["dev", "docs", "jupyter"]
files = [
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]
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[[package]]
name = "pywinpty"
@@ -3953,6 +5651,8 @@ version = "2.0.14"
description = "Pseudo terminal support for Windows from Python."
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
+markers = "os_name == \"nt\""
files = [
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@@ -3968,6 +5668,7 @@ version = "6.0.2"
description = "YAML parser and emitter for Python"
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
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@@ -4030,6 +5731,7 @@ version = "27.0.0"
description = "Python bindings for 0MQ"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "docs", "jupyter"]
files = [
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@@ -4121,6 +5823,7 @@ version = "0.35.1"
description = "JSON Referencing + Python"
optional = false
python-versions = ">=3.8"
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@@ -4136,6 +5839,7 @@ version = "2024.11.6"
description = "Alternative regular expression module, to replace re."
optional = false
python-versions = ">=3.8"
+groups = ["main", "docs"]
files = [
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@@ -4239,6 +5943,7 @@ version = "2.32.4"
description = "Python HTTP for Humans."
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
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@@ -4260,6 +5965,7 @@ version = "0.1.4"
description = "A pure python RFC3339 validator"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["dev", "jupyter"]
files = [
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@@ -4274,6 +5980,7 @@ version = "0.1.1"
description = "Pure python rfc3986 validator"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["dev", "jupyter"]
files = [
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@@ -4285,6 +5992,7 @@ version = "14.0.0"
description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
optional = false
python-versions = ">=3.8.0"
+groups = ["main", "dev"]
files = [
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@@ -4304,6 +6012,7 @@ version = "0.20.1"
description = "Python bindings to Rust's persistent data structures (rpds)"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "docs", "jupyter"]
files = [
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@@ -4410,12 +6119,62 @@ files = [
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]
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+optional = false
+python-versions = ">=3.7"
+groups = ["dev"]
+markers = "sys_platform != \"emscripten\""
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+[[package]]
+name = "safehttpx"
+version = "0.1.7"
+description = "A small Python library created to help developers protect their applications from Server Side Request Forgery (SSRF) attacks."
+optional = false
+python-versions = ">3.9"
+groups = ["dev"]
+markers = "python_version >= \"3.10\""
+files = [
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+
+[package.dependencies]
+httpx = "*"
+
+[package.extras]
+dev = ["pytest"]
+
[[package]]
name = "safetensors"
version = "0.5.3"
description = ""
optional = false
python-versions = ">=3.7"
+groups = ["main"]
files = [
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@@ -4447,21 +6206,38 @@ tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
testing = ["h5py (>=3.7.0)", "huggingface-hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools-rust (>=1.5.2)"]
torch = ["safetensors[numpy]", "torch (>=1.10)"]
+[[package]]
+name = "semantic-version"
+version = "2.10.0"
+description = "A library implementing the 'SemVer' scheme."
+optional = false
+python-versions = ">=2.7"
+groups = ["dev"]
+files = [
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+[package.extras]
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+doc = ["Sphinx", "sphinx-rtd-theme"]
+
[[package]]
name = "send2trash"
version = "1.8.3"
description = "Send file to trash natively under Mac OS X, Windows and Linux"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+groups = ["dev", "jupyter"]
files = [
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]
[package.extras]
-nativelib = ["pyobjc-framework-Cocoa", "pywin32"]
-objc = ["pyobjc-framework-Cocoa"]
-win32 = ["pywin32"]
+nativelib = ["pyobjc-framework-Cocoa ; sys_platform == \"darwin\"", "pywin32 ; sys_platform == \"win32\""]
+objc = ["pyobjc-framework-Cocoa ; sys_platform == \"darwin\""]
+win32 = ["pywin32 ; sys_platform == \"win32\""]
[[package]]
name = "sentencepiece"
@@ -4469,6 +6245,7 @@ version = "0.2.0"
description = "SentencePiece python wrapper"
optional = false
python-versions = "*"
+groups = ["main"]
files = [
{file = "sentencepiece-0.2.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:188779e1298a1c8b8253c7d3ad729cb0a9891e5cef5e5d07ce4592c54869e227"},
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@@ -4531,6 +6308,7 @@ version = "2.31.0"
description = "Python client for Sentry (https://sentry.io)"
optional = false
python-versions = ">=3.6"
+groups = ["main"]
files = [
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@@ -4587,6 +6365,7 @@ version = "1.3.6"
description = "A Python module to customize the process title"
optional = false
python-versions = ">=3.8"
+groups = ["main"]
files = [
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@@ -4696,19 +6475,21 @@ version = "75.3.2"
description = "Easily download, build, install, upgrade, and uninstall Python packages"
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev", "jupyter"]
files = [
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]
+markers = {main = "python_version >= \"3.9\" and platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\""}
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-core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.collections", "jaraco.functools", "jaraco.text (>=3.7)", "more-itertools", "more-itertools (>=8.8)", "packaging", "packaging (>=24)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\"", "ruff (>=0.5.2) ; sys_platform != \"cygwin\""]
+core = ["importlib-metadata (>=6) ; python_version < \"3.10\"", "importlib-resources (>=5.10.2) ; python_version < \"3.9\"", "jaraco.collections", "jaraco.functools", "jaraco.text (>=3.7)", "more-itertools", "more-itertools (>=8.8)", "packaging", "packaging (>=24)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1) ; python_version < \"3.11\"", "wheel (>=0.43.0)"]
cover = ["pytest-cov"]
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
enabler = ["pytest-enabler (>=2.2)"]
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-type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.12.*)", "pytest-mypy"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21) ; python_version >= \"3.9\" and sys_platform != \"cygwin\"", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test (>=5.5)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf ; sys_platform != \"cygwin\"", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "ruff (<=0.7.1)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+type = ["importlib-metadata (>=7.0.2) ; python_version < \"3.10\"", "jaraco.develop (>=7.21) ; sys_platform != \"cygwin\"", "mypy (==1.12.*)", "pytest-mypy"]
[[package]]
name = "shellingham"
@@ -4716,6 +6497,7 @@ version = "1.5.4"
description = "Tool to Detect Surrounding Shell"
optional = false
python-versions = ">=3.7"
+groups = ["dev"]
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@@ -4727,6 +6509,7 @@ version = "1.17.0"
description = "Python 2 and 3 compatibility utilities"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
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@@ -4738,6 +6521,7 @@ version = "5.0.2"
description = "A pure Python implementation of a sliding window memory map manager"
optional = false
python-versions = ">=3.7"
+groups = ["main"]
files = [
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@@ -4749,6 +6533,7 @@ version = "1.3.1"
description = "Sniff out which async library your code is running under"
optional = false
python-versions = ">=3.7"
+groups = ["dev", "jupyter"]
files = [
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@@ -4760,6 +6545,7 @@ version = "3.0.1"
description = "This package provides 32 stemmers for 30 languages generated from Snowball algorithms."
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*"
+groups = ["docs"]
files = [
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@@ -4771,6 +6557,7 @@ version = "2.7"
description = "A modern CSS selector implementation for Beautiful Soup."
optional = false
python-versions = ">=3.8"
+groups = ["dev", "docs", "jupyter"]
files = [
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@@ -4782,6 +6569,7 @@ version = "7.1.2"
description = "Python documentation generator"
optional = false
python-versions = ">=3.8"
+groups = ["docs"]
files = [
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@@ -4817,6 +6605,7 @@ version = "2021.3.14"
description = "Rebuild Sphinx documentation on changes, with live-reload in the browser."
optional = false
python-versions = ">=3.6"
+groups = ["docs"]
files = [
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@@ -4836,6 +6625,7 @@ version = "1.0.0b2"
description = "A modern skeleton for Sphinx themes."
optional = false
python-versions = ">=3.7"
+groups = ["docs"]
files = [
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@@ -4853,6 +6643,7 @@ version = "1.0.4"
description = "sphinxcontrib-applehelp is a Sphinx extension which outputs Apple help books"
optional = false
python-versions = ">=3.8"
+groups = ["docs"]
files = [
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@@ -4868,6 +6659,7 @@ version = "1.0.2"
description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp document."
optional = false
python-versions = ">=3.5"
+groups = ["docs"]
files = [
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@@ -4883,6 +6675,7 @@ version = "2.0.1"
description = "sphinxcontrib-htmlhelp is a sphinx extension which renders HTML help files"
optional = false
python-versions = ">=3.8"
+groups = ["docs"]
files = [
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@@ -4898,6 +6691,7 @@ version = "1.0.1"
description = "A sphinx extension which renders display math in HTML via JavaScript"
optional = false
python-versions = ">=3.5"
+groups = ["docs"]
files = [
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@@ -4912,6 +6706,7 @@ version = "0.7"
description = "Sphinx \"napoleon\" extension."
optional = false
python-versions = "*"
+groups = ["docs"]
files = [
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@@ -4927,6 +6722,7 @@ version = "1.0.3"
description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp document."
optional = false
python-versions = ">=3.5"
+groups = ["docs"]
files = [
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@@ -4942,6 +6738,7 @@ version = "1.1.5"
description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs \"serialized\" HTML files (json and pickle)."
optional = false
python-versions = ">=3.5"
+groups = ["docs"]
files = [
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@@ -4957,6 +6754,7 @@ version = "0.6.3"
description = "Extract data from python stack frames and tracebacks for informative displays"
optional = false
python-versions = "*"
+groups = ["dev", "jupyter"]
files = [
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@@ -4970,12 +6768,74 @@ pure-eval = "*"
[package.extras]
tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
+[[package]]
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+version = "0.44.0"
+description = "The little ASGI library that shines."
+optional = false
+python-versions = ">=3.8"
+groups = ["dev"]
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+
+[package.extras]
+full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
+
+[[package]]
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+groups = ["dev"]
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+[package.extras]
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+[[package]]
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+[package.extras]
+full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
+
[[package]]
name = "sympy"
version = "1.12.1"
description = "Computer algebra system (CAS) in Python"
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version == \"3.8\""
files = [
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@@ -4990,6 +6850,8 @@ version = "1.14.0"
description = "Computer algebra system (CAS) in Python"
optional = false
python-versions = ">=3.9"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.9\""
files = [
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@@ -5007,6 +6869,7 @@ version = "0.9.0"
description = "Pretty-print tabular data"
optional = false
python-versions = ">=3.7"
+groups = ["docs"]
files = [
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@@ -5021,6 +6884,7 @@ version = "0.18.1"
description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
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@@ -5042,6 +6906,7 @@ version = "0.7.0"
description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
optional = false
python-versions = ">=3.8"
+groups = ["docs"]
files = [
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@@ -5094,6 +6959,7 @@ version = "1.2.1"
description = "A tiny CSS parser"
optional = false
python-versions = ">=3.7"
+groups = ["dev", "docs", "jupyter"]
files = [
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@@ -5112,6 +6978,7 @@ version = "0.20.3"
description = ""
optional = false
python-versions = ">=3.7"
+groups = ["main"]
files = [
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docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
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-[package.extras]
-dev = ["tokenizers[testing]"]
-docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
-testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
-
[[package]]
name = "tomli"
version = "2.2.1"
description = "A lil' TOML parser"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
+markers = "python_version < \"3.11\""
files = [
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@@ -5308,12 +7145,27 @@ files = [
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]
+[[package]]
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+description = "Style preserving TOML library"
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+groups = ["dev"]
+markers = "python_version < \"3.10\""
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name = "tomlkit"
version = "0.13.3"
description = "Style preserving TOML library"
optional = false
python-versions = ">=3.8"
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files = [
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description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
optional = false
python-versions = ">=3.8.0"
+groups = ["main", "dev"]
+markers = "python_version == \"3.8\""
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@@ -5376,6 +7230,8 @@ version = "2.7.1"
description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
optional = false
python-versions = ">=3.9.0"
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+markers = "python_version >= \"3.9\""
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description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
optional = false
python-versions = ">=3.8"
+groups = ["dev", "docs", "jupyter"]
files = [
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@@ -5457,6 +7314,7 @@ version = "4.67.1"
description = "Fast, Extensible Progress Meter"
optional = false
python-versions = ">=3.7"
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description = "Traitlets Python configuration system"
optional = false
python-versions = ">=3.8"
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name = "transformers"
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description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
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python-versions = ">=3.8.0"
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torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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vision = ["Pillow (>=10.0.1,<=15.0)"]
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name = "transformers"
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description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
optional = false
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tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
tiktoken = ["blobfile", "tiktoken"]
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torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
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-video = ["av"]
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vision = ["Pillow (>=10.0.1,<=15.0)"]
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@@ -5633,6 +7494,7 @@ version = "0.0.5"
description = "This is a text generation method which returns a generator, streaming out each token in real-time during inference, based on Huggingface/Transformers."
optional = false
python-versions = ">=3.5"
+groups = ["main"]
files = [
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]
@@ -5646,6 +7508,8 @@ version = "3.1.0"
description = "A language and compiler for custom Deep Learning operations"
optional = false
python-versions = "*"
+groups = ["main", "dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version == \"3.8\""
files = [
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@@ -5668,6 +7532,8 @@ version = "3.3.1"
description = "A language and compiler for custom Deep Learning operations"
optional = false
python-versions = "*"
+groups = ["main", "dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.9\""
files = [
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@@ -5691,6 +7557,7 @@ version = "4.4.0"
description = "Run-time type checker for Python"
optional = false
python-versions = ">=3.8"
+groups = ["main"]
files = [
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@@ -5702,7 +7569,7 @@ typing-extensions = ">=4.10.0"
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doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.3.0)"]
-test = ["coverage[toml] (>=7)", "mypy (>=1.2.0)", "pytest (>=7)"]
+test = ["coverage[toml] (>=7)", "mypy (>=1.2.0) ; platform_python_implementation != \"PyPy\"", "pytest (>=7)"]
[[package]]
name = "typer"
@@ -5710,6 +7577,7 @@ version = "0.16.0"
description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
optional = false
python-versions = ">=3.7"
+groups = ["dev"]
files = [
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@@ -5727,6 +7595,7 @@ version = "2.9.0.20241206"
description = "Typing stubs for python-dateutil"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
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@@ -5738,6 +7607,7 @@ version = "4.13.2"
description = "Backported and Experimental Type Hints for Python 3.8+"
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
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@@ -5749,6 +7619,7 @@ version = "0.9.0"
description = "Runtime inspection utilities for typing module."
optional = false
python-versions = "*"
+groups = ["dev"]
files = [
{file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
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@@ -5764,6 +7635,7 @@ version = "2025.2"
description = "Provider of IANA time zone data"
optional = false
python-versions = ">=2"
+groups = ["main", "dev"]
files = [
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@@ -5775,6 +7647,7 @@ version = "1.3.0"
description = "RFC 6570 URI Template Processor"
optional = false
python-versions = ">=3.7"
+groups = ["dev", "jupyter"]
files = [
{file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"},
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@@ -5789,23 +7662,67 @@ version = "2.2.3"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
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]
[package.extras]
-brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
h2 = ["h2 (>=4,<5)"]
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
zstd = ["zstandard (>=0.18.0)"]
+[[package]]
+name = "uvicorn"
+version = "0.33.0"
+description = "The lightning-fast ASGI server."
+optional = false
+python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version == \"3.8\" and sys_platform != \"emscripten\""
+files = [
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+h11 = ">=0.8"
+typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
+
+[package.extras]
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+
+[[package]]
+name = "uvicorn"
+version = "0.38.0"
+description = "The lightning-fast ASGI server."
+optional = false
+python-versions = ">=3.9"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" and sys_platform != \"emscripten\""
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+h11 = ">=0.8"
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+
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+
[[package]]
name = "wandb"
version = "0.20.1"
description = "A CLI and library for interacting with the Weights & Biases API."
optional = false
python-versions = ">=3.8"
+groups = ["main"]
files = [
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@@ -5828,7 +7745,7 @@ packaging = "*"
platformdirs = "*"
protobuf = [
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{version = ">=3.15.0,<4.21.0 || >4.21.0,<5.28.0 || >5.28.0,<7", markers = "python_version == \"3.9\" and sys_platform == \"linux\""},
]
psutil = ">=5.0.0"
@@ -5845,7 +7762,7 @@ azure = ["azure-identity", "azure-storage-blob"]
gcp = ["google-cloud-storage"]
importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
-launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
+launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0) ; python_version >= \"3.9\"", "typing-extensions"]
media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
models = ["cloudpickle"]
perf = ["orjson"]
@@ -5858,6 +7775,7 @@ version = "0.2.13"
description = "Measures the displayed width of unicode strings in a terminal"
optional = false
python-versions = "*"
+groups = ["dev", "jupyter"]
files = [
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@@ -5869,6 +7787,7 @@ version = "24.8.0"
description = "A library for working with the color formats defined by HTML and CSS."
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
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@@ -5884,6 +7803,7 @@ version = "0.5.1"
description = "Character encoding aliases for legacy web content"
optional = false
python-versions = "*"
+groups = ["dev", "docs", "jupyter"]
files = [
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@@ -5895,6 +7815,7 @@ version = "1.8.0"
description = "WebSocket client for Python with low level API options"
optional = false
python-versions = ">=3.8"
+groups = ["dev", "jupyter"]
files = [
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optional = ["python-socks", "wsaccel"]
test = ["websockets"]
+[[package]]
+name = "websockets"
+version = "12.0"
+description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version < \"3.10\""
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files = [
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{file = "widgetsnbextension-4.0.14.tar.gz", hash = "sha256:a3629b04e3edb893212df862038c7232f62973373869db5084aed739b437b5af"},
@@ -5922,6 +8007,7 @@ version = "3.5.0"
description = "Python binding for xxHash"
optional = false
python-versions = ">=3.7"
+groups = ["main"]
files = [
{file = "xxhash-3.5.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ece616532c499ee9afbb83078b1b952beffef121d989841f7f4b3dc5ac0fd212"},
{file = "xxhash-3.5.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3171f693dbc2cef6477054a665dc255d996646b4023fe56cb4db80e26f4cc520"},
@@ -6054,6 +8140,7 @@ version = "1.15.2"
description = "Yet another URL library"
optional = false
python-versions = ">=3.8"
+groups = ["main"]
files = [
{file = "yarl-1.15.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e4ee8b8639070ff246ad3649294336b06db37a94bdea0d09ea491603e0be73b8"},
{file = "yarl-1.15.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a7cf963a357c5f00cb55b1955df8bbe68d2f2f65de065160a1c26b85a1e44172"},
@@ -6166,20 +8253,22 @@ version = "3.20.2"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
+groups = ["main", "dev", "docs", "jupyter"]
files = [
{file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
{file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
]
+markers = {main = "python_version < \"3.10\"", docs = "python_version < \"3.10\"", jupyter = "python_version < \"3.10\""}
[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
cover = ["pytest-cov"]
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+test = ["big-O", "importlib-resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
type = ["pytest-mypy"]
[metadata]
-lock-version = "2.0"
+lock-version = "2.1"
python-versions = ">=3.8,<4.0"
-content-hash = "7572fab0b6315f222cb5a72895eefa24ac9ca8b7e91a0a418e21c226072474cf"
+content-hash = "d8a486530e7bd458603318ae85fdf80745bb172faa7017a6065c3a7347e692d3"
diff --git a/pyproject.toml b/pyproject.toml
index 9fcce44d4..07a6d09ae 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,6 +19,7 @@
datasets=">=2.7.1"
einops=">=0.6.0"
fancy-einsum=">=0.0.3"
+ huggingface-hub=">=0.23.2,<1.0"
jaxtyping=">=0.2.11"
numpy=[
{version=">=1.20,<1.25", python=">=3.8,<3.9"},
@@ -26,12 +27,13 @@
{version=">=1.26,<2", python=">=3.12,<3.13"},
]
pandas=">=1.1.5"
+ protobuf=">=3.20.0"
python=">=3.8,<4.0"
rich=">=12.6.0"
sentencepiece="*"
torch=[{version="<2.6", python=">=3.8,<3.9"}, {version=">=2.6", python=">=3.9"}]
tqdm=">=4.64.1"
- transformers=[{version="<4.51", python=">=3.8,<3.9"}, {version=">=4.51", python=">=3.9"}]
+ transformers=[{version="<4.46.0", python=">=3.8,<3.9"}, {version="4.46.3", python=">=3.9"}]
transformers-stream-generator="^0.0.5"
typeguard="^4.2"
typing-extensions="*"
@@ -41,6 +43,7 @@
[tool.poetry.group.dev.dependencies]
black="^23.3.0"
circuitsvis=">=1.38.1"
+ gradio=">=4.0.0"
isort="5.8.0"
jupyter=">=1.0.0"
mypy=">=1.10.0"
diff --git a/tests/unit/pretrained_weight_conversions/test_gemma.py b/tests/unit/pretrained_weight_conversions/test_gemma.py
new file mode 100644
index 000000000..e7d7ea214
--- /dev/null
+++ b/tests/unit/pretrained_weight_conversions/test_gemma.py
@@ -0,0 +1,321 @@
+"""
+Unit tests for Gemma weight conversion.
+
+Tests cover:
+1. Multimodal vs text-only model detection
+2. Weight extraction from multimodal models
+3. Q/K normalization weight loading
+4. Device compatibility (MPS/CUDA)
+"""
+
+import torch
+import torch.nn as nn
+
+from transformer_lens.HookedTransformerConfig import HookedTransformerConfig
+from transformer_lens.pretrained.weight_conversions.gemma import convert_gemma_weights
+
+
+def get_gemma3_config(
+ n_layers: int = 2,
+ use_qk_norm: bool = True,
+ use_normalization_before_and_after: bool = True,
+):
+ """Create a Gemma 3 style config for testing."""
+ return HookedTransformerConfig(
+ d_model=128,
+ d_head=64,
+ n_heads=2,
+ n_key_value_heads=1,
+ d_mlp=512,
+ n_ctx=128,
+ n_layers=n_layers,
+ d_vocab=1024,
+ act_fn="gelu_pytorch_tanh",
+ use_qk_norm=use_qk_norm,
+ use_normalization_before_and_after=use_normalization_before_and_after,
+ normalization_type="RMS",
+ positional_embedding_type="rotary",
+ gated_mlp=True,
+ )
+
+
+class MockGemmaLayer(nn.Module):
+ """A mock Gemma layer with real nn.Module components."""
+
+ def __init__(self, cfg: HookedTransformerConfig, use_qk_norm: bool = True):
+ super().__init__()
+ self.input_layernorm = nn.LayerNorm(cfg.d_model)
+ self.post_attention_layernorm = nn.LayerNorm(cfg.d_model)
+
+ if cfg.use_normalization_before_and_after:
+ self.pre_feedforward_layernorm = nn.LayerNorm(cfg.d_model)
+ self.post_feedforward_layernorm = nn.LayerNorm(cfg.d_model)
+
+ # Self attention
+ self.self_attn = nn.Module()
+ self.self_attn.q_proj = nn.Linear(cfg.d_model, cfg.n_heads * cfg.d_head, bias=False)
+ self.self_attn.k_proj = nn.Linear(
+ cfg.d_model, cfg.n_key_value_heads * cfg.d_head, bias=False
+ )
+ self.self_attn.v_proj = nn.Linear(
+ cfg.d_model, cfg.n_key_value_heads * cfg.d_head, bias=False
+ )
+ self.self_attn.o_proj = nn.Linear(cfg.n_heads * cfg.d_head, cfg.d_model, bias=False)
+
+ # Q/K norms (Gemma 3)
+ if use_qk_norm:
+ self.self_attn.q_norm = nn.LayerNorm(cfg.d_head)
+ self.self_attn.k_norm = nn.LayerNorm(cfg.d_head)
+
+ # MLP
+ self.mlp = nn.Module()
+ self.mlp.up_proj = nn.Linear(cfg.d_model, cfg.d_mlp, bias=False)
+ self.mlp.gate_proj = nn.Linear(cfg.d_model, cfg.d_mlp, bias=False)
+ self.mlp.down_proj = nn.Linear(cfg.d_mlp, cfg.d_model, bias=False)
+
+
+class MockGemmaTextModel(nn.Module):
+ """A mock text-only Gemma3ForCausalLM model."""
+
+ def __init__(self, cfg: HookedTransformerConfig):
+ super().__init__()
+ self.model = nn.Module()
+ self.model.embed_tokens = nn.Embedding(cfg.d_vocab, cfg.d_model)
+ self.model.norm = nn.LayerNorm(cfg.d_model)
+ self.model.layers = nn.ModuleList(
+ [MockGemmaLayer(cfg, use_qk_norm=cfg.use_qk_norm) for _ in range(cfg.n_layers)]
+ )
+ self.lm_head = nn.Linear(cfg.d_model, cfg.d_vocab, bias=False)
+
+
+class MockGemmaMultimodalModel(nn.Module):
+ """A mock multimodal Gemma3ForConditionalGeneration model."""
+
+ def __init__(self, cfg: HookedTransformerConfig):
+ super().__init__()
+ # Multimodal structure: model.language_model.model
+ self.language_model = nn.Module()
+ self.language_model.model = nn.Module()
+
+ base = self.language_model.model
+ base.embed_tokens = nn.Embedding(cfg.d_vocab, cfg.d_model)
+ base.norm = nn.LayerNorm(cfg.d_model)
+ base.layers = nn.ModuleList(
+ [MockGemmaLayer(cfg, use_qk_norm=cfg.use_qk_norm) for _ in range(cfg.n_layers)]
+ )
+ # Note: No lm_head in multimodal, uses tied embeddings
+
+
+# ============================================================================
+# Test: Text-only model weight conversion
+# ============================================================================
+
+
+class TestGemmaTextOnlyConversion:
+ """Test weight conversion for text-only Gemma3ForCausalLM models."""
+
+ def test_convert_weights_basic(self):
+ """Test basic weight conversion produces expected keys."""
+ cfg = get_gemma3_config()
+ model = MockGemmaTextModel(cfg)
+
+ state_dict = convert_gemma_weights(model, cfg)
+
+ # Check essential keys exist
+ assert "embed.W_E" in state_dict
+ assert "ln_final.w" in state_dict
+ assert "unembed.W_U" in state_dict
+ assert "unembed.b_U" in state_dict
+
+ def test_convert_weights_layer_keys(self):
+ """Test that layer-specific keys are generated."""
+ cfg = get_gemma3_config(n_layers=2)
+ model = MockGemmaTextModel(cfg)
+
+ state_dict = convert_gemma_weights(model, cfg)
+
+ for layer in range(cfg.n_layers):
+ # Attention weights
+ assert f"blocks.{layer}.attn.W_Q" in state_dict
+ assert f"blocks.{layer}.attn._W_K" in state_dict
+ assert f"blocks.{layer}.attn._W_V" in state_dict
+ assert f"blocks.{layer}.attn.W_O" in state_dict
+
+ # Attention biases
+ assert f"blocks.{layer}.attn.b_Q" in state_dict
+ assert f"blocks.{layer}.attn._b_K" in state_dict
+ assert f"blocks.{layer}.attn._b_V" in state_dict
+ assert f"blocks.{layer}.attn.b_O" in state_dict
+
+ # MLP weights
+ assert f"blocks.{layer}.mlp.W_in" in state_dict
+ assert f"blocks.{layer}.mlp.W_gate" in state_dict
+ assert f"blocks.{layer}.mlp.W_out" in state_dict
+
+ # Layer norms
+ assert f"blocks.{layer}.ln1.w" in state_dict
+ assert f"blocks.{layer}.ln2.w" in state_dict
+
+ def test_convert_weights_qk_norm(self):
+ """Test that Q/K norm weights are extracted when use_qk_norm=True."""
+ cfg = get_gemma3_config(use_qk_norm=True)
+ model = MockGemmaTextModel(cfg)
+
+ state_dict = convert_gemma_weights(model, cfg)
+
+ for layer in range(cfg.n_layers):
+ assert f"blocks.{layer}.attn.q_norm.w" in state_dict
+ assert f"blocks.{layer}.attn.k_norm.w" in state_dict
+
+ def test_convert_weights_no_qk_norm(self):
+ """Test that Q/K norm weights are not extracted when use_qk_norm=False."""
+ cfg = get_gemma3_config(use_qk_norm=False)
+ model = MockGemmaTextModel(cfg)
+
+ state_dict = convert_gemma_weights(model, cfg)
+
+ for layer in range(cfg.n_layers):
+ assert f"blocks.{layer}.attn.q_norm.w" not in state_dict
+ assert f"blocks.{layer}.attn.k_norm.w" not in state_dict
+
+ def test_convert_weights_normalization_before_and_after(self):
+ """Test Gemma 2/3 style normalization weights."""
+ cfg = get_gemma3_config(use_normalization_before_and_after=True)
+ model = MockGemmaTextModel(cfg)
+
+ state_dict = convert_gemma_weights(model, cfg)
+
+ for layer in range(cfg.n_layers):
+ # Post-attention and post-feedforward norms (Gemma 2/3 style)
+ assert f"blocks.{layer}.ln1_post.w" in state_dict
+ assert f"blocks.{layer}.ln2_post.w" in state_dict
+
+
+# ============================================================================
+# Test: Multimodal model weight conversion
+# ============================================================================
+
+
+class TestGemmaMultimodalConversion:
+ """Test weight conversion for multimodal Gemma3ForConditionalGeneration models."""
+
+ def test_multimodal_detection(self):
+ """Test that multimodal models are correctly detected."""
+ cfg = get_gemma3_config()
+ model = MockGemmaMultimodalModel(cfg)
+
+ # Should have language_model attribute
+ assert hasattr(model, "language_model")
+
+ # Should produce valid state dict
+ state_dict = convert_gemma_weights(model, cfg)
+ assert "embed.W_E" in state_dict
+
+ def test_multimodal_embedding_extraction(self):
+ """Test that embeddings are extracted from language_model.model."""
+ cfg = get_gemma3_config()
+ model = MockGemmaMultimodalModel(cfg)
+
+ state_dict = convert_gemma_weights(model, cfg)
+
+ # Check embedding exists and has correct shape
+ assert "embed.W_E" in state_dict
+ assert state_dict["embed.W_E"].shape[0] == cfg.d_vocab
+
+ def test_multimodal_tied_embeddings_for_unembed(self):
+ """Test that multimodal models use tied embeddings for unembed."""
+ cfg = get_gemma3_config()
+ model = MockGemmaMultimodalModel(cfg)
+
+ state_dict = convert_gemma_weights(model, cfg)
+
+ # Unembed should exist
+ assert "unembed.W_U" in state_dict
+
+
+# ============================================================================
+# Test: Weight shapes
+# ============================================================================
+
+
+class TestGemmaWeightShapes:
+ """Test that converted weights have correct shapes."""
+
+ def test_attention_weight_shapes(self):
+ """Test attention weight shapes are correct."""
+ cfg = get_gemma3_config()
+ model = MockGemmaTextModel(cfg)
+
+ state_dict = convert_gemma_weights(model, cfg)
+
+ # W_Q: [n_heads, d_model, d_head]
+ assert state_dict["blocks.0.attn.W_Q"].shape == (cfg.n_heads, cfg.d_model, cfg.d_head)
+
+ # W_K/V with GQA: [n_key_value_heads, d_model, d_head]
+ assert state_dict["blocks.0.attn._W_K"].shape == (
+ cfg.n_key_value_heads,
+ cfg.d_model,
+ cfg.d_head,
+ )
+ assert state_dict["blocks.0.attn._W_V"].shape == (
+ cfg.n_key_value_heads,
+ cfg.d_model,
+ cfg.d_head,
+ )
+
+ # W_O: [n_heads, d_head, d_model]
+ assert state_dict["blocks.0.attn.W_O"].shape == (cfg.n_heads, cfg.d_head, cfg.d_model)
+
+ def test_mlp_weight_shapes(self):
+ """Test MLP weight shapes are correct."""
+ cfg = get_gemma3_config()
+ model = MockGemmaTextModel(cfg)
+
+ state_dict = convert_gemma_weights(model, cfg)
+
+ # W_in and W_gate: [d_model, d_mlp]
+ assert state_dict["blocks.0.mlp.W_in"].shape == (cfg.d_model, cfg.d_mlp)
+ assert state_dict["blocks.0.mlp.W_gate"].shape == (cfg.d_model, cfg.d_mlp)
+
+ # W_out: [d_mlp, d_model]
+ assert state_dict["blocks.0.mlp.W_out"].shape == (cfg.d_mlp, cfg.d_model)
+
+ def test_embedding_scaling(self):
+ """Test that embeddings are scaled by sqrt(d_model)."""
+ cfg = get_gemma3_config()
+ model = MockGemmaTextModel(cfg)
+
+ original_embed = model.model.embed_tokens.weight.clone()
+ state_dict = convert_gemma_weights(model, cfg)
+
+ # Embedding should be scaled by sqrt(d_model)
+ expected_scale = cfg.d_model**0.5
+ # Check approximately equal (allowing for dtype conversion)
+ ratio = state_dict["embed.W_E"] / original_embed
+ assert torch.allclose(ratio, torch.full_like(ratio, expected_scale), rtol=1e-4)
+
+
+# ============================================================================
+# Test: Device consistency
+# ============================================================================
+
+
+class TestGemmaDeviceConsistency:
+ """Test that bias tensors are created on the correct device."""
+
+ def test_bias_tensors_have_matching_device(self):
+ """Test that zero bias tensors match weight tensor devices."""
+ cfg = get_gemma3_config()
+ model = MockGemmaTextModel(cfg)
+
+ state_dict = convert_gemma_weights(model, cfg)
+
+ # Check that bias tensors are on the same device as their weights
+ for layer in range(cfg.n_layers):
+ w_q = state_dict[f"blocks.{layer}.attn.W_Q"]
+ b_q = state_dict[f"blocks.{layer}.attn.b_Q"]
+ assert w_q.device == b_q.device
+
+ w_out = state_dict[f"blocks.{layer}.mlp.W_out"]
+ b_out = state_dict[f"blocks.{layer}.mlp.b_out"]
+ assert w_out.device == b_out.device
diff --git a/tests/unit/test_gemma3_config.py b/tests/unit/test_gemma3_config.py
new file mode 100644
index 000000000..a0e5e958b
--- /dev/null
+++ b/tests/unit/test_gemma3_config.py
@@ -0,0 +1,442 @@
+"""
+Unit tests for Gemma 3 and MedGemma model support.
+
+Tests cover:
+1. Configuration generation for all Gemma 3 model variants
+2. Weight conversion from HuggingFace format
+3. Hybrid local/global attention configuration
+4. Per-layer RoPE base support
+"""
+
+from unittest import mock
+
+import pytest
+
+from transformer_lens.HookedTransformerConfig import HookedTransformerConfig
+from transformer_lens.loading_from_pretrained import (
+ OFFICIAL_MODEL_NAMES,
+ get_pretrained_model_config,
+)
+
+# ============================================================================
+# Test Data
+# ============================================================================
+
+GEMMA3_MODELS = [
+ "google/gemma-3-270m",
+ "google/gemma-3-270m-it",
+ "google/gemma-3-1b-pt",
+ "google/gemma-3-1b-it",
+ "google/gemma-3-4b-pt",
+ "google/gemma-3-4b-it",
+ "google/gemma-3-12b-pt",
+ "google/gemma-3-12b-it",
+ "google/gemma-3-27b-pt",
+ "google/gemma-3-27b-it",
+]
+
+MEDGEMMA_MODELS = [
+ "google/medgemma-4b-pt",
+ "google/medgemma-4b-it",
+ "google/medgemma-27b-it",
+ "google/medgemma-27b-text-it",
+]
+
+GEMMA3_CONFIG_SPECS = {
+ "270m": {
+ "d_model": 640,
+ "n_heads": 4,
+ "n_layers": 18,
+ "d_head": 256,
+ "n_key_value_heads": 1,
+ "d_mlp": 2048,
+ "window_size": 512,
+ },
+ "1b": {
+ "d_model": 1152,
+ "n_heads": 4,
+ "n_layers": 26,
+ "d_head": 256,
+ "n_key_value_heads": 1,
+ "d_mlp": 6912,
+ "window_size": 512,
+ },
+ "4b": {
+ "d_model": 2560,
+ "n_heads": 8,
+ "n_layers": 34,
+ "d_head": 256,
+ "n_key_value_heads": 4,
+ "d_mlp": 10240,
+ "window_size": 1024,
+ },
+ "12b": {
+ "d_model": 3840,
+ "n_heads": 16,
+ "n_layers": 48,
+ "d_head": 256,
+ "n_key_value_heads": 8,
+ "d_mlp": 15360,
+ "window_size": 1024,
+ },
+ "27b": {
+ "d_model": 5376,
+ "n_heads": 32,
+ "n_layers": 62,
+ "d_head": 128,
+ "n_key_value_heads": 16,
+ "d_mlp": 21504,
+ "window_size": 1024,
+ },
+}
+
+
+# ============================================================================
+# Test: Model names in official list
+# ============================================================================
+
+
+class TestGemma3ModelRegistration:
+ """Test that all Gemma 3 and MedGemma models are registered in OFFICIAL_MODEL_NAMES."""
+
+ @pytest.mark.parametrize("model_name", GEMMA3_MODELS)
+ def test_gemma3_models_in_official_list(self, model_name: str):
+ assert model_name in OFFICIAL_MODEL_NAMES, f"{model_name} should be in OFFICIAL_MODEL_NAMES"
+
+ @pytest.mark.parametrize("model_name", MEDGEMMA_MODELS)
+ def test_medgemma_models_in_official_list(self, model_name: str):
+ assert model_name in OFFICIAL_MODEL_NAMES, f"{model_name} should be in OFFICIAL_MODEL_NAMES"
+
+
+# ============================================================================
+# Test: Configuration generation
+# ============================================================================
+
+
+class TestGemma3ConfigGeneration:
+ """Test that get_pretrained_model_config generates correct configs for Gemma 3."""
+
+ @pytest.fixture
+ def mock_hf_config(self):
+ """Create a minimal mock HuggingFace config."""
+ config = mock.Mock()
+ config.architectures = ["Gemma3ForCausalLM"]
+ return config
+
+ @pytest.mark.parametrize(
+ "model_name,size_key",
+ [
+ ("google/gemma-3-270m", "270m"),
+ ("google/gemma-3-270m-it", "270m"),
+ ("google/gemma-3-1b-pt", "1b"),
+ ("google/gemma-3-1b-it", "1b"),
+ ],
+ )
+ def test_gemma3_small_model_config(self, model_name: str, size_key: str, mock_hf_config):
+ """Test configuration for small Gemma 3 models (270M, 1B)."""
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config(model_name)
+
+ expected = GEMMA3_CONFIG_SPECS[size_key]
+ assert cfg.d_model == expected["d_model"]
+ assert cfg.n_heads == expected["n_heads"]
+ assert cfg.n_layers == expected["n_layers"]
+ assert cfg.d_head == expected["d_head"]
+ assert cfg.n_key_value_heads == expected["n_key_value_heads"]
+ assert cfg.d_mlp == expected["d_mlp"]
+
+ @pytest.mark.parametrize(
+ "model_name,size_key",
+ [
+ ("google/gemma-3-4b-pt", "4b"),
+ ("google/gemma-3-4b-it", "4b"),
+ ("google/medgemma-4b-pt", "4b"),
+ ("google/medgemma-4b-it", "4b"),
+ ],
+ )
+ def test_gemma3_4b_model_config(self, model_name: str, size_key: str, mock_hf_config):
+ """Test configuration for 4B models (Gemma 3 and MedGemma)."""
+ mock_hf_config.architectures = ["Gemma3ForConditionalGeneration"]
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config(model_name)
+
+ expected = GEMMA3_CONFIG_SPECS[size_key]
+ assert cfg.d_model == expected["d_model"]
+ assert cfg.n_heads == expected["n_heads"]
+ assert cfg.n_layers == expected["n_layers"]
+ assert cfg.n_key_value_heads == expected["n_key_value_heads"]
+
+
+# ============================================================================
+# Test: Hybrid attention configuration
+# ============================================================================
+
+
+class TestGemma3HybridAttention:
+ """Test hybrid local/global attention configuration (5:1 pattern)."""
+
+ @pytest.fixture
+ def mock_hf_config(self):
+ config = mock.Mock()
+ config.architectures = ["Gemma3ForCausalLM"]
+ return config
+
+ def test_attn_types_pattern_270m(self, mock_hf_config):
+ """Test 5:1 local/global pattern for 270M (18 layers)."""
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config("google/gemma-3-270m")
+
+ assert cfg.use_local_attn is True
+ assert cfg.attn_types is not None
+ assert len(cfg.attn_types) == 18
+
+ # Check 5:1 pattern: global at indices 5, 11, 17
+ for i, attn_type in enumerate(cfg.attn_types):
+ expected = "global" if (i + 1) % 6 == 0 else "local"
+ assert attn_type == expected, f"Layer {i}: expected {expected}, got {attn_type}"
+
+ def test_attn_types_pattern_1b(self, mock_hf_config):
+ """Test 5:1 local/global pattern for 1B (26 layers)."""
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config("google/gemma-3-1b-pt")
+
+ assert cfg.use_local_attn is True
+ assert len(cfg.attn_types) == 26
+
+ # Count global layers
+ global_count = cfg.attn_types.count("global")
+ local_count = cfg.attn_types.count("local")
+ assert global_count == 4 # 26 // 6 = 4 global layers
+ assert local_count == 22
+
+ def test_window_size_small_models(self, mock_hf_config):
+ """Test that 270M/1B models use 512 token window."""
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config("google/gemma-3-270m")
+ assert cfg.window_size == 512
+
+ def test_window_size_large_models(self, mock_hf_config):
+ """Test that 4B+ models use 1024 token window."""
+ mock_hf_config.architectures = ["Gemma3ForConditionalGeneration"]
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config("google/gemma-3-4b-pt")
+ assert cfg.window_size == 1024
+
+
+# ============================================================================
+# Test: Per-layer RoPE base
+# ============================================================================
+
+
+class TestGemma3PerLayerRoPE:
+ """Test per-layer RoPE base configuration."""
+
+ @pytest.fixture
+ def mock_hf_config(self):
+ config = mock.Mock()
+ config.architectures = ["Gemma3ForCausalLM"]
+ return config
+
+ def test_rotary_base_global(self, mock_hf_config):
+ """Test that global attention layers use 1M RoPE base."""
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config("google/gemma-3-270m")
+
+ assert cfg.rotary_base == 1_000_000
+
+ def test_rotary_base_local(self, mock_hf_config):
+ """Test that local attention layers use 10K RoPE base."""
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config("google/gemma-3-270m")
+
+ assert cfg.rotary_base_local == 10_000
+
+
+# ============================================================================
+# Test: Q/K Normalization
+# ============================================================================
+
+
+class TestGemma3QKNorm:
+ """Test Q/K normalization configuration."""
+
+ @pytest.fixture
+ def mock_hf_config(self):
+ config = mock.Mock()
+ config.architectures = ["Gemma3ForCausalLM"]
+ return config
+
+ def test_use_qk_norm_enabled(self, mock_hf_config):
+ """Test that Q/K normalization is enabled for all Gemma 3 models."""
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config("google/gemma-3-270m")
+
+ assert cfg.use_qk_norm is True
+
+
+# ============================================================================
+# Test: Normalization before and after
+# ============================================================================
+
+
+class TestGemma3Normalization:
+ """Test Gemma 2/3 style normalization (before and after blocks)."""
+
+ @pytest.fixture
+ def mock_hf_config(self):
+ config = mock.Mock()
+ config.architectures = ["Gemma3ForCausalLM"]
+ return config
+
+ def test_normalization_before_and_after(self, mock_hf_config):
+ """Test that use_normalization_before_and_after is enabled."""
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config("google/gemma-3-270m")
+
+ assert cfg.use_normalization_before_and_after is True
+
+
+# ============================================================================
+# Test: Vocabulary size
+# ============================================================================
+
+
+class TestGemma3VocabSize:
+ """Test vocabulary size configuration."""
+
+ @pytest.fixture
+ def mock_hf_config(self):
+ config = mock.Mock()
+ config.architectures = ["Gemma3ForCausalLM"]
+ return config
+
+ def test_vocab_size_small_models(self, mock_hf_config):
+ """Test vocab size for 270M/1B models."""
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config("google/gemma-3-270m")
+ assert cfg.d_vocab == 262144
+
+ def test_vocab_size_multimodal_models(self, mock_hf_config):
+ """Test vocab size for 4B+ multimodal models (262208)."""
+ mock_hf_config.architectures = ["Gemma3ForConditionalGeneration"]
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config("google/gemma-3-4b-pt")
+ assert cfg.d_vocab == 262208
+
+ def test_vocab_size_medgemma_text_only(self, mock_hf_config):
+ """Test vocab size for MedGemma 27B text-only variant (262144)."""
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config("google/medgemma-27b-text-it")
+ assert cfg.d_vocab == 262144
+
+
+# ============================================================================
+# Test: Default context length
+# ============================================================================
+
+
+class TestGemma3ContextLength:
+ """Test default context length configuration."""
+
+ @pytest.fixture
+ def mock_hf_config(self):
+ config = mock.Mock()
+ config.architectures = ["Gemma3ForCausalLM"]
+ return config
+
+ def test_default_context_length(self, mock_hf_config):
+ """Test that default n_ctx is 8192 (memory-safe default)."""
+ with mock.patch(
+ "transformer_lens.loading_from_pretrained.AutoConfig.from_pretrained",
+ return_value=mock_hf_config,
+ ):
+ cfg = get_pretrained_model_config("google/gemma-3-270m")
+ assert cfg.n_ctx == 8192
+
+
+# ============================================================================
+# Test: HookedTransformerConfig with rotary_base_local
+# ============================================================================
+
+
+class TestHookedTransformerConfigRotaryBaseLocal:
+ """Test that HookedTransformerConfig supports rotary_base_local."""
+
+ def test_rotary_base_local_default_none(self):
+ """Test that rotary_base_local defaults to None."""
+ cfg = HookedTransformerConfig(
+ d_model=128,
+ d_head=32,
+ n_heads=4,
+ n_ctx=128,
+ n_layers=2,
+ attn_only=True,
+ )
+ assert cfg.rotary_base_local is None
+
+ def test_rotary_base_local_can_be_set(self):
+ """Test that rotary_base_local can be set to a custom value."""
+ cfg = HookedTransformerConfig(
+ d_model=128,
+ d_head=32,
+ n_heads=4,
+ n_ctx=128,
+ n_layers=2,
+ attn_only=True,
+ rotary_base_local=10000,
+ )
+ assert cfg.rotary_base_local == 10000
+
+ def test_rotary_base_and_rotary_base_local_coexist(self):
+ """Test that both rotary_base and rotary_base_local can be set."""
+ cfg = HookedTransformerConfig(
+ d_model=128,
+ d_head=32,
+ n_heads=4,
+ n_ctx=128,
+ n_layers=2,
+ attn_only=True,
+ rotary_base=1000000,
+ rotary_base_local=10000,
+ )
+ assert cfg.rotary_base == 1000000
+ assert cfg.rotary_base_local == 10000
diff --git a/transformer_lens/HookedTransformer.py b/transformer_lens/HookedTransformer.py
index 025b43793..296bf0282 100644
--- a/transformer_lens/HookedTransformer.py
+++ b/transformer_lens/HookedTransformer.py
@@ -1273,6 +1273,12 @@ def from_pretrained(
default_padding_side: Which side to pad on when tokenizing. Defaults to
"right".
first_n_layers: If specified, only load the first n layers of the model.
+ n_ctx: If specified, override the default context length for the model.
+ This is particularly useful for models with very large default context lengths
+ (e.g., Gemma 3 models support up to 32K-131K) to reduce memory usage on consumer
+ hardware. The default context lengths for Gemma 3 models are set to 8K for safe
+ loading. Pass n_ctx=16384, n_ctx=32768, etc. to use larger context windows if
+ you have sufficient memory.
"""
if model_name.lower().startswith("t5"):
raise RuntimeError(
@@ -2303,11 +2309,13 @@ def generate(
top_p=top_p,
temperature=temperature,
freq_penalty=freq_penalty,
- tokens=torch.cat(
- (input_tokens, torch.cat(sampled_tokens_list, dim=1)), dim=1
- )
- if "sampled_tokens" in locals()
- else input_tokens,
+ tokens=(
+ torch.cat(
+ (input_tokens, torch.cat(sampled_tokens_list, dim=1)), dim=1
+ )
+ if "sampled_tokens" in locals()
+ else input_tokens
+ ),
).to(devices.get_device_for_block_index(0, self.cfg))
else:
sampled_tokens = utils.sample_logits(
diff --git a/transformer_lens/HookedTransformerConfig.py b/transformer_lens/HookedTransformerConfig.py
index 9cf16f578..f788c29ab 100644
--- a/transformer_lens/HookedTransformerConfig.py
+++ b/transformer_lens/HookedTransformerConfig.py
@@ -192,6 +192,12 @@ class HookedTransformerConfig:
NTK_by_parts_factor (float): The overall factor used in the "NTK-by-parts" method that
affects the rate of change between low and high-frequency interpolation strategies.
Defaults to 8.0.
+ use_qk_norm (bool): Whether to apply RMSNorm to the query and key projections before
+ computing attention scores. Used by Gemma 3 models. Defaults to False.
+ rotary_base_local (int, *optional*): The base for rotary positional embeddings in local
+ attention layers. Used by models with hybrid local/global attention (e.g., Gemma 3)
+ which use different RoPE bases for local (10k) and global (1M) attention. Defaults
+ to None, which means the standard rotary_base is used for all layers.
"""
@@ -247,6 +253,9 @@ class HookedTransformerConfig:
n_key_value_heads: Optional[int] = None
post_embedding_ln: bool = False
rotary_base: int = 10000
+ rotary_base_local: Optional[
+ int
+ ] = None # For models with different RoPE bases per attention type (e.g., Gemma 3)
trust_remote_code: bool = False
rotary_adjacent_pairs: bool = False
load_in_4bit: bool = False
diff --git a/transformer_lens/components/abstract_attention.py b/transformer_lens/components/abstract_attention.py
index 0aee43814..848af70c8 100644
--- a/transformer_lens/components/abstract_attention.py
+++ b/transformer_lens/components/abstract_attention.py
@@ -137,10 +137,15 @@ def __init__(
self.hook_rot_q = HookPoint()
if self.cfg.rotary_dim is None: # keep mypy happy
raise ValueError("Rotary dim must be provided for rotary positional embeddings")
+ # Use per-layer RoPE base if specified (e.g., Gemma 3 uses 10k for local, 1M for global)
+ if self.cfg.rotary_base_local is not None and self.attn_type == "local":
+ rope_base = self.cfg.rotary_base_local
+ else:
+ rope_base = self.cfg.rotary_base
sin, cos = self.calculate_sin_cos_rotary(
self.cfg.rotary_dim,
self.cfg.n_ctx,
- base=self.cfg.rotary_base,
+ base=rope_base,
dtype=self.cfg.dtype,
)
self.register_buffer("rotary_sin", sin)
diff --git a/transformer_lens/components/attention.py b/transformer_lens/components/attention.py
index 50520f07f..f0a7a5e45 100644
--- a/transformer_lens/components/attention.py
+++ b/transformer_lens/components/attention.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`Attention`.
"""
+
from typing import Dict, Optional, Union
import torch
diff --git a/transformer_lens/components/bert_block.py b/transformer_lens/components/bert_block.py
index 528432b3c..e527a88fc 100644
--- a/transformer_lens/components/bert_block.py
+++ b/transformer_lens/components/bert_block.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`BertBlock`.
"""
+
from typing import Optional
import torch
diff --git a/transformer_lens/components/bert_embed.py b/transformer_lens/components/bert_embed.py
index 4033ed9c3..c1ff3e7ef 100644
--- a/transformer_lens/components/bert_embed.py
+++ b/transformer_lens/components/bert_embed.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`BertEmbed`.
"""
+
from typing import Dict, Optional, Union
import einops
diff --git a/transformer_lens/components/bert_mlm_head.py b/transformer_lens/components/bert_mlm_head.py
index 2cfe5a9d8..6e797a0e1 100644
--- a/transformer_lens/components/bert_mlm_head.py
+++ b/transformer_lens/components/bert_mlm_head.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`BertMLMHead`.
"""
+
from typing import Dict, Union
import torch
diff --git a/transformer_lens/components/bert_nsp_head.py b/transformer_lens/components/bert_nsp_head.py
index 6cddb217f..2af50ee23 100644
--- a/transformer_lens/components/bert_nsp_head.py
+++ b/transformer_lens/components/bert_nsp_head.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`BertNSPHead`.
"""
+
from typing import Dict, Union
import torch
diff --git a/transformer_lens/components/bert_pooler.py b/transformer_lens/components/bert_pooler.py
index cd205bf7f..5f41ecb03 100644
--- a/transformer_lens/components/bert_pooler.py
+++ b/transformer_lens/components/bert_pooler.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`BertPooler`.
"""
+
from typing import Dict, Union
import torch
diff --git a/transformer_lens/components/embed.py b/transformer_lens/components/embed.py
index 97bec7f67..88bcaba99 100644
--- a/transformer_lens/components/embed.py
+++ b/transformer_lens/components/embed.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`Embed`.
"""
+
from typing import Dict, Union
import torch
diff --git a/transformer_lens/components/layer_norm.py b/transformer_lens/components/layer_norm.py
index 35d7816b7..b19aa7919 100644
--- a/transformer_lens/components/layer_norm.py
+++ b/transformer_lens/components/layer_norm.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`LayerNorm`.
"""
+
from typing import Dict, Optional, Union
import torch
diff --git a/transformer_lens/components/layer_norm_pre.py b/transformer_lens/components/layer_norm_pre.py
index 914abb0d5..8efc186e6 100644
--- a/transformer_lens/components/layer_norm_pre.py
+++ b/transformer_lens/components/layer_norm_pre.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`LayerNormPre`.
"""
+
from typing import Dict, Union
import torch
diff --git a/transformer_lens/components/mlps/can_be_used_as_mlp.py b/transformer_lens/components/mlps/can_be_used_as_mlp.py
index b0945276b..bf51a3a28 100644
--- a/transformer_lens/components/mlps/can_be_used_as_mlp.py
+++ b/transformer_lens/components/mlps/can_be_used_as_mlp.py
@@ -1,9 +1,10 @@
"""Can Be Used as MLP component.
This module serves as the base for everything within TransformerLens that can be used like an MLP.
-This does not necessarily mean that every component extending this class will be an MLP, but
+This does not necessarily mean that every component extending this class will be an MLP, but
everything extending this class can be used interchangeably for an MLP.
"""
+
from typing import Dict, Optional, Union
import torch
diff --git a/transformer_lens/components/mlps/gated_mlp.py b/transformer_lens/components/mlps/gated_mlp.py
index 1386a157c..f80d35454 100644
--- a/transformer_lens/components/mlps/gated_mlp.py
+++ b/transformer_lens/components/mlps/gated_mlp.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`GatedMLP`.
"""
+
from typing import Dict, Union
import torch
diff --git a/transformer_lens/components/mlps/gated_mlp_4bit.py b/transformer_lens/components/mlps/gated_mlp_4bit.py
index 708a7d12f..f493b147c 100644
--- a/transformer_lens/components/mlps/gated_mlp_4bit.py
+++ b/transformer_lens/components/mlps/gated_mlp_4bit.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`GatedMLP`.
"""
+
from typing import Dict, Union
import torch
diff --git a/transformer_lens/components/pos_embed.py b/transformer_lens/components/pos_embed.py
index 629d4eb1e..786dbb946 100644
--- a/transformer_lens/components/pos_embed.py
+++ b/transformer_lens/components/pos_embed.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`PosEmbed`.
"""
+
from typing import Dict, Optional, Union
import einops
diff --git a/transformer_lens/components/rms_norm.py b/transformer_lens/components/rms_norm.py
index 9867fa626..7f45b6e55 100644
--- a/transformer_lens/components/rms_norm.py
+++ b/transformer_lens/components/rms_norm.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`RMSNorm`.
"""
+
from typing import Dict, Optional, Union
import torch
diff --git a/transformer_lens/components/rms_norm_pre.py b/transformer_lens/components/rms_norm_pre.py
index 2d2ff5793..53bef360b 100644
--- a/transformer_lens/components/rms_norm_pre.py
+++ b/transformer_lens/components/rms_norm_pre.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`RMSNormPre`.
"""
+
from typing import Dict, Union
import torch
diff --git a/transformer_lens/components/token_typed_embed.py b/transformer_lens/components/token_typed_embed.py
index 015255bcf..be063990c 100644
--- a/transformer_lens/components/token_typed_embed.py
+++ b/transformer_lens/components/token_typed_embed.py
@@ -2,6 +2,7 @@
This module contains all the component :class:`TokenTypeEmbed`.
"""
+
from typing import Dict, Union
import torch
diff --git a/transformer_lens/factories/mlp_factory.py b/transformer_lens/factories/mlp_factory.py
index de873b091..fe4dbbab7 100644
--- a/transformer_lens/factories/mlp_factory.py
+++ b/transformer_lens/factories/mlp_factory.py
@@ -2,6 +2,7 @@
Centralized location for creating any MLP needed within TransformerLens
"""
+
from transformer_lens.components.mlps.can_be_used_as_mlp import CanBeUsedAsMLP
from transformer_lens.components.mlps.gated_mlp import GatedMLP
from transformer_lens.components.mlps.gated_mlp_4bit import GatedMLP4Bit
diff --git a/transformer_lens/loading_from_pretrained.py b/transformer_lens/loading_from_pretrained.py
index 8bfb6315d..ebb7b5e81 100644
--- a/transformer_lens/loading_from_pretrained.py
+++ b/transformer_lens/loading_from_pretrained.py
@@ -255,6 +255,20 @@
"google/gemma-2-9b-it",
"google/gemma-2-27b",
"google/gemma-2-27b-it",
+ "google/gemma-3-270m",
+ "google/gemma-3-270m-it",
+ "google/gemma-3-1b-pt",
+ "google/gemma-3-1b-it",
+ "google/gemma-3-4b-pt",
+ "google/gemma-3-4b-it",
+ "google/gemma-3-12b-pt",
+ "google/gemma-3-12b-it",
+ "google/gemma-3-27b-pt",
+ "google/gemma-3-27b-it",
+ "google/medgemma-4b-pt",
+ "google/medgemma-4b-it",
+ "google/medgemma-27b-it",
+ "google/medgemma-27b-text-it",
"01-ai/Yi-6B",
"01-ai/Yi-34B",
"01-ai/Yi-6B-Chat",
@@ -711,6 +725,20 @@
"google/gemma-2-9b-it": ["gemma-2-9b-it"],
"google/gemma-2-27b": ["gemma-2-27b"],
"google/gemma-2-27b-it": ["gemma-2-27b-it"],
+ "google/gemma-3-270m": ["gemma-3-270m"],
+ "google/gemma-3-270m-it": ["gemma-3-270m-it"],
+ "google/gemma-3-1b-pt": ["gemma-3-1b-pt"],
+ "google/gemma-3-1b-it": ["gemma-3-1b-it"],
+ "google/gemma-3-4b-pt": ["gemma-3-4b-pt"],
+ "google/gemma-3-4b-it": ["gemma-3-4b-it"],
+ "google/gemma-3-12b-pt": ["gemma-3-12b-pt"],
+ "google/gemma-3-12b-it": ["gemma-3-12b-it"],
+ "google/gemma-3-27b-pt": ["gemma-3-27b-pt"],
+ "google/gemma-3-27b-it": ["gemma-3-27b-it"],
+ "google/medgemma-4b-pt": ["medgemma-4b-pt"],
+ "google/medgemma-4b-it": ["medgemma-4b-it"],
+ "google/medgemma-27b-it": ["medgemma-27b-it"],
+ "google/medgemma-27b-text-it": ["medgemma-27b-text-it"],
"01-ai/Yi-6B": ["yi-6b", "Yi-6B"],
"01-ai/Yi-34B": ["yi-34b", "Yi-34B"],
"01-ai/Yi-6B-Chat": ["yi-6b-chat", "Yi-6B-Chat"],
@@ -790,6 +818,17 @@ def convert_hf_model_config(model_name: str, **kwargs: Any):
# Load HuggingFace model config
if "llama" in official_model_name.lower():
architecture = "LlamaForCausalLM"
+ elif "gemma-3" in official_model_name.lower() or "medgemma" in official_model_name.lower():
+ # Gemma 3: 270M and 1B are text-only (CausalLM), 4B+ are multimodal (ConditionalGeneration)
+ # Exception: medgemma-27b-text-it is text-only
+ if "270m" in official_model_name.lower() or "1b" in official_model_name.lower():
+ architecture = "Gemma3ForCausalLM"
+ elif "medgemma-27b-text" in official_model_name.lower():
+ # medgemma-27b-text-it is text-only variant
+ architecture = "Gemma3ForCausalLM"
+ else:
+ # 4B, 12B, 27B and medgemma are multimodal
+ architecture = "Gemma3ForConditionalGeneration"
elif "gemma-2" in official_model_name.lower():
architecture = "Gemma2ForCausalLM"
elif "gemma" in official_model_name.lower():
@@ -1354,11 +1393,13 @@ def convert_hf_model_config(model_name: str, **kwargs: Any):
elif architecture == "Qwen3ForCausalLM":
cfg_dict = {
"d_model": hf_config.hidden_size,
- "d_head": hf_config.head_dim
- if hasattr(hf_config, "head_dim")
- and hf_config.head_dim is not None
- and hf_config.head_dim > 0
- else hf_config.hidden_size // hf_config.num_attention_heads,
+ "d_head": (
+ hf_config.head_dim
+ if hasattr(hf_config, "head_dim")
+ and hf_config.head_dim is not None
+ and hf_config.head_dim > 0
+ else hf_config.hidden_size // hf_config.num_attention_heads
+ ),
"n_heads": hf_config.num_attention_heads,
"n_key_value_heads": (
hf_config.num_key_value_heads
@@ -1377,9 +1418,11 @@ def convert_hf_model_config(model_name: str, **kwargs: Any):
"positional_embedding_type": "rotary",
"rotary_base": int(hf_config.rope_theta),
"rotary_adjacent_pairs": False,
- "rotary_dim": hf_config.head_dim
- if hasattr(hf_config, "head_dim") and hf_config.head_dim > 0
- else hf_config.hidden_size // hf_config.num_attention_heads,
+ "rotary_dim": (
+ hf_config.head_dim
+ if hasattr(hf_config, "head_dim") and hf_config.head_dim > 0
+ else hf_config.hidden_size // hf_config.num_attention_heads
+ ),
"tokenizer_prepends_bos": True,
"final_rms": True,
"gated_mlp": True,
@@ -1437,6 +1480,341 @@ def convert_hf_model_config(model_name: str, **kwargs: Any):
"rotary_dim": hf_config.hidden_size // hf_config.num_attention_heads,
}
+ elif official_model_name.startswith("google/gemma-3-270m"):
+ # Architecture for Gemma-3 270m and Gemma-3 270m Instruct models
+ cfg_dict = {
+ "d_model": 640,
+ "d_head": 256,
+ "n_heads": 4,
+ "d_mlp": 2048,
+ "n_layers": 18,
+ "n_ctx": 8192, # Safe default (model supports up to 32K). Override: cfg_kwargs={"n_ctx": 32768}
+ "eps": 1e-06,
+ "d_vocab": 262144,
+ "act_fn": "gelu_pytorch_tanh",
+ "initializer_range": 0.02,
+ "normalization_type": "RMS",
+ "rotary_base": 1000000, # Global attention layers
+ "rotary_base_local": 10000, # Local attention layers (per Gemma 3 paper)
+ "positional_embedding_type": "rotary",
+ "use_attn_scale": True,
+ "n_key_value_heads": 1,
+ "gated_mlp": True,
+ "final_rms": True,
+ "use_normalization_before_and_after": True,
+ "use_qk_norm": True,
+ "window_size": 512,
+ "use_local_attn": True,
+ "attn_types": [
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ ],
+ }
+ elif official_model_name.startswith("google/gemma-3-1b"):
+ # Architecture for Gemma-3 1b-pt and Gemma-3 1b-it models
+ cfg_dict = {
+ "d_model": 1152,
+ "d_head": 256,
+ "n_heads": 4,
+ "d_mlp": 6912,
+ "n_layers": 26,
+ "n_ctx": 8192, # Safe default (model supports up to 32K). Override: cfg_kwargs={"n_ctx": 32768}
+ "eps": 1e-06,
+ "d_vocab": 262144,
+ "act_fn": "gelu_pytorch_tanh",
+ "initializer_range": 0.02,
+ "normalization_type": "RMS",
+ "rotary_base": 1000000, # Global attention layers
+ "rotary_base_local": 10000, # Local attention layers (per Gemma 3 paper)
+ "positional_embedding_type": "rotary",
+ "use_attn_scale": True,
+ "n_key_value_heads": 1,
+ "gated_mlp": True,
+ "final_rms": True,
+ "use_normalization_before_and_after": True,
+ "use_qk_norm": True,
+ "window_size": 512,
+ "use_local_attn": True,
+ "attn_types": [
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ ],
+ }
+ elif official_model_name.startswith("google/gemma-3-4b") or official_model_name.startswith(
+ "google/medgemma-4b"
+ ):
+ # Architecture for Gemma-3 4b and MedGemma 4b models (multimodal, text-only extraction)
+ cfg_dict = {
+ "d_model": 2560,
+ "d_head": 256,
+ "n_heads": 8,
+ "d_mlp": 10240,
+ "n_layers": 34,
+ "n_ctx": 8192, # Safe default (model supports up to 128K). Override: cfg_kwargs={"n_ctx": 131072}
+ "eps": 1e-06,
+ "d_vocab": 262208,
+ "act_fn": "gelu_pytorch_tanh",
+ "initializer_range": 0.02,
+ "normalization_type": "RMS",
+ "rotary_base": 1000000, # Global attention layers
+ "rotary_base_local": 10000, # Local attention layers (per Gemma 3 paper)
+ "positional_embedding_type": "rotary",
+ "use_attn_scale": True,
+ "n_key_value_heads": 4,
+ "gated_mlp": True,
+ "final_rms": True,
+ "use_normalization_before_and_after": True,
+ "use_qk_norm": True,
+ "window_size": 1024,
+ "use_local_attn": True,
+ "attn_types": [
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ ],
+ }
+ elif official_model_name.startswith("google/gemma-3-12b"):
+ # Architecture for Gemma-3 12b models (multimodal, text-only extraction)
+ cfg_dict = {
+ "d_model": 3840,
+ "d_head": 256,
+ "n_heads": 16,
+ "d_mlp": 15360,
+ "n_layers": 48,
+ "n_ctx": 8192, # Safe default (model supports up to 128K). Override: cfg_kwargs={"n_ctx": 131072}
+ "eps": 1e-06,
+ "d_vocab": 262208,
+ "act_fn": "gelu_pytorch_tanh",
+ "initializer_range": 0.02,
+ "normalization_type": "RMS",
+ "rotary_base": 1000000, # Global attention layers
+ "rotary_base_local": 10000, # Local attention layers (per Gemma 3 paper)
+ "positional_embedding_type": "rotary",
+ "use_attn_scale": True,
+ "n_key_value_heads": 8,
+ "gated_mlp": True,
+ "final_rms": True,
+ "use_normalization_before_and_after": True,
+ "use_qk_norm": True,
+ "window_size": 1024,
+ "use_local_attn": True,
+ "attn_types": [
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ ],
+ }
+ elif official_model_name.startswith("google/gemma-3-27b") or official_model_name.startswith(
+ "google/medgemma-27b"
+ ):
+ # Architecture for Gemma-3 27b and MedGemma 27b models (multimodal/text-only extraction)
+ # Note: medgemma-27b-text-it uses Gemma3ForCausalLM (text-only), others use Gemma3ForConditionalGeneration
+ cfg_dict = {
+ "d_model": 5376,
+ "d_head": 128,
+ "n_heads": 32,
+ "d_mlp": 21504,
+ "n_layers": 62,
+ "n_ctx": 8192, # Safe default (model supports up to 128K). Override: cfg_kwargs={"n_ctx": 131072}
+ "eps": 1e-06,
+ "d_vocab": (
+ 262144 if official_model_name == "google/medgemma-27b-text-it" else 262208
+ ), # text-only variant uses 262144
+ "act_fn": "gelu_pytorch_tanh",
+ "initializer_range": 0.02,
+ "normalization_type": "RMS",
+ "rotary_base": 1000000, # Global attention layers
+ "rotary_base_local": 10000, # Local attention layers (per Gemma 3 paper)
+ "positional_embedding_type": "rotary",
+ "use_attn_scale": True,
+ "n_key_value_heads": 16,
+ "gated_mlp": True,
+ "final_rms": True,
+ "use_normalization_before_and_after": True,
+ "use_qk_norm": True,
+ "window_size": 1024,
+ "use_local_attn": True,
+ "attn_types": [
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ "local",
+ "local",
+ "local",
+ "global",
+ "local",
+ "local",
+ ],
+ }
elif official_model_name.startswith("google/gemma-2b"):
# Architecture for Gemma 2b and Gemma 2b Instruct models
cfg_dict = {
@@ -1643,6 +2021,7 @@ def get_pretrained_model_config(
default_prepend_bos: Optional[bool] = None,
dtype: torch.dtype = torch.float32,
first_n_layers: Optional[int] = None,
+ n_ctx: Optional[int] = None,
**kwargs: Any,
):
"""Returns the pretrained model config as an HookedTransformerConfig object.
@@ -1681,6 +2060,11 @@ def get_pretrained_model_config(
so this empirically seems to give better results. Note that you can also locally override the default behavior
by passing in prepend_bos=True/False when you call a method that processes the input string.
dtype (torch.dtype, optional): The dtype to load the TransformerLens model in.
+ first_n_layers (int, optional): If specified, only load the first n layers of the model.
+ n_ctx (int, optional): Override the model's default context length. Useful for extending
+ context beyond the default safe value (e.g., using 16K or 32K for Gemma 3 models that
+ default to 8K for memory efficiency). Be aware that larger context lengths require
+ significantly more RAM.
kwargs: Other optional arguments passed to HuggingFace's from_pretrained.
Also given to other HuggingFace functions when compatible.
@@ -1772,6 +2156,10 @@ def get_pretrained_model_config(
if first_n_layers is not None:
cfg_dict["n_layers"] = first_n_layers
+ if n_ctx is not None:
+ # User explicitly overrode the context length
+ cfg_dict["n_ctx"] = n_ctx
+
cfg = HookedTransformerConfig.from_dict(cfg_dict)
return cfg
@@ -1858,6 +2246,11 @@ def get_pretrained_state_dict(
if "torch_dtype" in kwargs:
dtype = kwargs["torch_dtype"]
del kwargs["torch_dtype"]
+ if "hf_token" in kwargs:
+ del kwargs["hf_token"]
+ if "n_ctx" in kwargs:
+ # n_ctx is handled in get_pretrained_model_config, don't pass to HuggingFace
+ del kwargs["n_ctx"]
if Path(official_model_name).exists():
official_model_name = str(Path(official_model_name).resolve())
logging.info(f"Loading model from local path {official_model_name}")
@@ -1935,6 +2328,16 @@ def get_pretrained_state_dict(
token=huggingface_token if len(huggingface_token) > 0 else None,
**kwargs,
)
+ elif cfg.original_architecture == "Gemma3ForConditionalGeneration":
+ # Multimodal Gemma 3 models - use AutoModel
+ from transformers import AutoModel
+
+ hf_model = AutoModel.from_pretrained(
+ official_model_name,
+ torch_dtype=dtype,
+ token=huggingface_token if len(huggingface_token) > 0 else None,
+ **kwargs,
+ )
else:
hf_model = AutoModelForCausalLM.from_pretrained(
official_model_name,
@@ -1986,6 +2389,11 @@ def get_pretrained_state_dict(
state_dict = convert_gemma_weights(hf_model, cfg)
elif cfg.original_architecture == "Gemma2ForCausalLM":
state_dict = convert_gemma_weights(hf_model, cfg)
+ elif cfg.original_architecture == "Gemma3ForCausalLM":
+ state_dict = convert_gemma_weights(hf_model, cfg)
+ elif cfg.original_architecture == "Gemma3ForConditionalGeneration":
+ # Multimodal model - extract text-only weights
+ state_dict = convert_gemma_weights(hf_model, cfg)
else:
raise ValueError(
f"Loading weights from the architecture is not currently supported: {cfg.original_architecture}, generated from model name {cfg.model_name}. Feel free to open an issue on GitHub to request this feature."
diff --git a/transformer_lens/past_key_value_caching.py b/transformer_lens/past_key_value_caching.py
index 2f904b927..ec6abb4fd 100644
--- a/transformer_lens/past_key_value_caching.py
+++ b/transformer_lens/past_key_value_caching.py
@@ -4,6 +4,7 @@
classes, which are used to store past keys and values for the Transformer. This is important for
generating text - we can cache a lot of past computation and avoid repeating ourselves!
"""
+
from dataclasses import dataclass
from typing import List, Union
diff --git a/transformer_lens/patching.py b/transformer_lens/patching.py
index aff08dae0..deccf898e 100644
--- a/transformer_lens/patching.py
+++ b/transformer_lens/patching.py
@@ -22,10 +22,10 @@
- We can then iterate over many
possible activations and look at how much they affect the corrupted run. If patching in an
activation significantly increases the probability of the correct answer, this allows us to
- localise which activations matter.
+ localise which activations matter.
- A key detail is that we move a single activation __from__ the clean run __to __the corrupted run.
So if this changes the answer from incorrect to correct, we can be confident that the activation
- moved was important.
+ moved was important.
Intuition:
diff --git a/transformer_lens/pretrained/weight_conversions/gemma.py b/transformer_lens/pretrained/weight_conversions/gemma.py
index 0c46bea11..983cab59f 100644
--- a/transformer_lens/pretrained/weight_conversions/gemma.py
+++ b/transformer_lens/pretrained/weight_conversions/gemma.py
@@ -10,31 +10,50 @@ def convert_gemma_weights(gemma, cfg: HookedTransformerConfig):
assert cfg.n_key_value_heads is not None # keep mypy happy
assert cfg.d_mlp is not None # keep mypy happy
+ # Check if this is a multimodal model (Gemma3ForConditionalGeneration)
+ # Multimodal models have language_model attribute, text-only models don't
+ is_multimodal = hasattr(gemma, "language_model")
+
+ # Get the actual model
+ # For multimodal: gemma.language_model.model is Gemma3TextModel which has layers/embed_tokens
+ # For text-only: gemma has .model which contains layers/embed_tokens
+ if is_multimodal:
+ # Multimodal structure: gemma.language_model.model contains the text transformer
+ # We skip gemma.vision_tower entirely to save memory
+ if hasattr(gemma.language_model, "model"):
+ base_model = gemma.language_model.model
+ else:
+ # Fallback if structure is different
+ base_model = gemma.language_model
+ else:
+ # Text-only Gemma3ForCausalLM has .model wrapper
+ base_model = gemma.model
+
# Gemma Models scale embeddings by multiplying by sqrt(d_model), use hidden state type to match
# HF implementation
- state_dict["embed.W_E"] = gemma.model.embed_tokens.weight * torch.tensor(
+ state_dict["embed.W_E"] = base_model.embed_tokens.weight * torch.tensor(
cfg.d_model**0.5, dtype=cfg.dtype
)
# Gemma has no biases anywhere
for l in range(cfg.n_layers):
# GemmaRMSNorm adds 1 to weights before multiplying by input, keep RMS calcs in float32
- state_dict[f"blocks.{l}.ln1.w"] = gemma.model.layers[
+ state_dict[f"blocks.{l}.ln1.w"] = base_model.layers[
l
].input_layernorm.weight.float() + torch.ones_like(
- gemma.model.layers[l].input_layernorm.weight, dtype=torch.float32
+ base_model.layers[l].input_layernorm.weight, dtype=torch.float32
)
if cfg.use_normalization_before_and_after:
# Only applies for Gemma 2
- state_dict[f"blocks.{l}.ln1_post.w"] = gemma.model.layers[
+ state_dict[f"blocks.{l}.ln1_post.w"] = base_model.layers[
l
].post_attention_layernorm.weight.float() + torch.ones_like(
- gemma.model.layers[l].input_layernorm.weight, dtype=torch.float32
+ base_model.layers[l].input_layernorm.weight, dtype=torch.float32
)
- W_Q = gemma.model.layers[l].self_attn.q_proj.weight
- W_K = gemma.model.layers[l].self_attn.k_proj.weight
- W_V = gemma.model.layers[l].self_attn.v_proj.weight
+ W_Q = base_model.layers[l].self_attn.q_proj.weight
+ W_K = base_model.layers[l].self_attn.k_proj.weight
+ W_V = base_model.layers[l].self_attn.v_proj.weight
W_Q = einops.rearrange(W_Q, "(n h) m->n m h", n=cfg.n_heads)
W_K = einops.rearrange(W_K, "(n h) m->n m h", n=cfg.n_key_value_heads)
W_V = einops.rearrange(W_V, "(n h) m->n m h", n=cfg.n_key_value_heads)
@@ -42,54 +61,74 @@ def convert_gemma_weights(gemma, cfg: HookedTransformerConfig):
state_dict[f"blocks.{l}.attn._W_K"] = W_K
state_dict[f"blocks.{l}.attn._W_V"] = W_V
- state_dict[f"blocks.{l}.attn.b_Q"] = torch.zeros(cfg.n_heads, cfg.d_head, dtype=cfg.dtype)
+ # Load q_norm and k_norm if they exist (Gemma 3)
+ if cfg.use_qk_norm:
+ state_dict[f"blocks.{l}.attn.q_norm.w"] = base_model.layers[l].self_attn.q_norm.weight
+ state_dict[f"blocks.{l}.attn.k_norm.w"] = base_model.layers[l].self_attn.k_norm.weight
+
+ state_dict[f"blocks.{l}.attn.b_Q"] = torch.zeros(
+ cfg.n_heads, cfg.d_head, dtype=cfg.dtype, device=W_Q.device
+ )
state_dict[f"blocks.{l}.attn._b_K"] = torch.zeros(
- cfg.n_key_value_heads, cfg.d_head, dtype=cfg.dtype
+ cfg.n_key_value_heads, cfg.d_head, dtype=cfg.dtype, device=W_K.device
)
state_dict[f"blocks.{l}.attn._b_V"] = torch.zeros(
- cfg.n_key_value_heads, cfg.d_head, dtype=cfg.dtype
+ cfg.n_key_value_heads, cfg.d_head, dtype=cfg.dtype, device=W_V.device
)
- W_O = gemma.model.layers[l].self_attn.o_proj.weight
+ W_O = base_model.layers[l].self_attn.o_proj.weight
W_O = einops.rearrange(W_O, "m (n h)->n h m", n=cfg.n_heads)
state_dict[f"blocks.{l}.attn.W_O"] = W_O
- state_dict[f"blocks.{l}.attn.b_O"] = torch.zeros(cfg.d_model, dtype=cfg.dtype)
+ state_dict[f"blocks.{l}.attn.b_O"] = torch.zeros(
+ cfg.d_model, dtype=cfg.dtype, device=W_O.device
+ )
# GemmaRMSNorm adds 1 to weights before multiplying by input, keep RMS calcs in float32
if not cfg.use_normalization_before_and_after:
# Only applies for Gemma 1. Confusingly post_attention_layernorm is applied to mlp_input in Gemma 1 and attn_out in Gemma 2
- state_dict[f"blocks.{l}.ln2.w"] = gemma.model.layers[
+ state_dict[f"blocks.{l}.ln2.w"] = base_model.layers[
l
].post_attention_layernorm.weight.float() + torch.ones_like(
- gemma.model.norm.weight, dtype=torch.float32
+ base_model.norm.weight, dtype=torch.float32
)
else:
# Only applies for Gemma 2
- state_dict[f"blocks.{l}.ln2.w"] = gemma.model.layers[
+ state_dict[f"blocks.{l}.ln2.w"] = base_model.layers[
l
].pre_feedforward_layernorm.weight.float() + torch.ones_like(
- gemma.model.layers[l].pre_feedforward_layernorm.weight, dtype=torch.float32
+ base_model.layers[l].pre_feedforward_layernorm.weight, dtype=torch.float32
)
- state_dict[f"blocks.{l}.ln2_post.w"] = gemma.model.layers[
+ state_dict[f"blocks.{l}.ln2_post.w"] = base_model.layers[
l
].post_feedforward_layernorm.weight.float() + torch.ones_like(
- gemma.model.layers[l].post_feedforward_layernorm.weight, dtype=torch.float32
+ base_model.layers[l].post_feedforward_layernorm.weight, dtype=torch.float32
)
- state_dict[f"blocks.{l}.mlp.W_in"] = gemma.model.layers[l].mlp.up_proj.weight.T
- state_dict[f"blocks.{l}.mlp.W_gate"] = gemma.model.layers[l].mlp.gate_proj.weight.T
- state_dict[f"blocks.{l}.mlp.b_in"] = torch.zeros(cfg.d_mlp, dtype=cfg.dtype)
+ state_dict[f"blocks.{l}.mlp.W_in"] = base_model.layers[l].mlp.up_proj.weight.T
+ state_dict[f"blocks.{l}.mlp.W_gate"] = base_model.layers[l].mlp.gate_proj.weight.T
+ state_dict[f"blocks.{l}.mlp.b_in"] = torch.zeros(
+ cfg.d_mlp, dtype=cfg.dtype, device=base_model.layers[l].mlp.up_proj.weight.device
+ )
- state_dict[f"blocks.{l}.mlp.W_out"] = gemma.model.layers[l].mlp.down_proj.weight.T
- state_dict[f"blocks.{l}.mlp.b_out"] = torch.zeros(cfg.d_model, dtype=cfg.dtype)
+ state_dict[f"blocks.{l}.mlp.W_out"] = base_model.layers[l].mlp.down_proj.weight.T
+ state_dict[f"blocks.{l}.mlp.b_out"] = torch.zeros(
+ cfg.d_model, dtype=cfg.dtype, device=base_model.layers[l].mlp.down_proj.weight.device
+ )
# GemmaRMSNorm adds 1 to weights before multiplying by input, keep RMS calcs in float32
- state_dict["ln_final.w"] = gemma.model.norm.weight.float() + torch.ones_like(
- gemma.model.norm.weight, dtype=torch.float32
+ state_dict["ln_final.w"] = base_model.norm.weight.float() + torch.ones_like(
+ base_model.norm.weight, dtype=torch.float32
)
- state_dict["unembed.W_U"] = gemma.lm_head.weight.T
- state_dict["unembed.b_U"] = torch.zeros(cfg.d_vocab, dtype=cfg.dtype)
+ # For multimodal models, lm_head might not exist or be tied to embeddings
+ if hasattr(gemma, "lm_head"):
+ state_dict["unembed.W_U"] = gemma.lm_head.weight.T
+ unembed_device = gemma.lm_head.weight.device
+ else:
+ # Multimodal models might use tied embeddings
+ state_dict["unembed.W_U"] = base_model.embed_tokens.weight.T
+ unembed_device = base_model.embed_tokens.weight.device
+ state_dict["unembed.b_U"] = torch.zeros(cfg.d_vocab, dtype=cfg.dtype, device=unembed_device)
return state_dict
diff --git a/transformer_lens/utilities/activation_functions.py b/transformer_lens/utilities/activation_functions.py
index 6cc701360..4cecf3988 100644
--- a/transformer_lens/utilities/activation_functions.py
+++ b/transformer_lens/utilities/activation_functions.py
@@ -2,6 +2,7 @@
Utilities for interacting with all supported activation functions.
"""
+
from typing import Callable, Dict
import torch
diff --git a/transformer_lens/utilities/addmm.py b/transformer_lens/utilities/addmm.py
index 6f86b5508..647443972 100644
--- a/transformer_lens/utilities/addmm.py
+++ b/transformer_lens/utilities/addmm.py
@@ -2,6 +2,7 @@
Implementations of Addmm functions matching Huggingface implementations.
"""
+
import torch
from jaxtyping import Float
diff --git a/transformer_lens/utils.py b/transformer_lens/utils.py
index c0992848a..79682fe2d 100644
--- a/transformer_lens/utils.py
+++ b/transformer_lens/utils.py
@@ -170,7 +170,7 @@ def lm_accuracy(
def gelu_new(
- input: Float[torch.Tensor, "batch pos d_mlp"]
+ input: Float[torch.Tensor, "batch pos d_mlp"],
) -> Float[torch.Tensor, "batch pos d_mlp"]:
# Implementation of GeLU used by GPT2 - subtly different from PyTorch's
return (
@@ -181,7 +181,7 @@ def gelu_new(
def gelu_fast(
- input: Float[torch.Tensor, "batch pos d_mlp"]
+ input: Float[torch.Tensor, "batch pos d_mlp"],
) -> Float[torch.Tensor, "batch pos d_mlp"]:
return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)))