ℹ️ Codacy found a Code Style issues

Co-authored-by: codacy-production[bot] <61871480+codacy-production[bot]@users.noreply.github.com>

Author: shoutsid

app/models/llama/llama.py

@@ -12,13 +12,65 @@
 import functools, sys, argparse, json, os
 import numpy as np
 np.set_printoptions(linewidth=200)
+from typing import Tuple, Dict
+
+from tinygrad.helpers import Timing, DEBUG, dtypes, CI
+from tinygrad.ops import Device
+from tinygrad.tensor import Tensor
+from tinygrad.nn import Embedding, Linear
+import functools, sys, argparse, json, os
+import numpy as np
+np.set_printoptions(linewidth=200)
 from typing import Optional, Tuple, Dict
 
 from tinygrad.helpers import Timing, DEBUG, dtypes, CI
 from tinygrad.ops import Device
 from tinygrad.tensor import Tensor
 from tinygrad.nn import Embedding, Linear
 from tinygrad.ops import GlobalCounters
+import functools, sys, argparse, json, os
+import numpy as np
+np.set_printoptions(linewidth=200)
+from typing import Tuple, Dict
+
+from tinygrad.helpers import Timing, DEBUG, dtypes, CI
+from tinygrad.ops import Device
+from tinygrad.tensor import Tensor
+from tinygrad.nn import Embedding, Linear
+from pathlib import Path
+import functools, sys, argparse, json, os
+import numpy as np
+np.set_printoptions(linewidth=200)
+from typing import Tuple, Dict
+
+from tinygrad.helpers import Timing, DEBUG, dtypes, CI
+from tinygrad.ops import Device
+from tinygrad.tensor import Tensor
+from tinygrad.nn import Embedding, Linear
+from tinygrad.ops import GlobalCounters
+from tinygrad.jit import TinyJit, JIT_SUPPORTED_DEVICE
+from tinygrad.shape.symbolic import Variable, sym_infer
+
+from tinygrad.helpers import Timing, DEBUG, dtypes, CI
+from tinygrad.ops import Device
+from tinygrad.tensor import Tensor
+from tinygrad.nn import Embedding, Linear
+from pathlib import Path
+import functools, sys, argparse, json, os
+import numpy as np
+np.set_printoptions(linewidth=200)
+from typing import Optional, Tuple, Dict
+
+from tinygrad.ops import GlobalCounters
+from tinygrad.jit import TinyJit, JIT_SUPPORTED_DEVICE
+from tinygrad.shape.symbolic import Variable, sym_infer
+
+from sentencepiece import SentencePieceProcessor
+from tinygrad.tensor import Tensor
+from tinygrad.ops import Device
+from tinygrad.tensor import Tensor
+from tinygrad.nn import Embedding
+from tinygrad.ops import GlobalCounters
 from tinygrad.jit import TinyJit, JIT_SUPPORTED_DEVICE
 from tinygrad.shape.symbolic import Variable, sym_infer
 

app/models/llama/utils.py

@@ -152,10 +152,12 @@ def convert_from_huggingface(weights, model):
     """
     keymap = {
         "model.embed_tokens.weight": "tok_embeddings.weight",
-        **{f"model.layers.{l}.input_layernorm.weight": f"layers.{l}.attention_norm.weight" for l in range(len(model.layers))},
+        **{f"model.layers.{layer_index}.input_layernorm.weight": f"layers.{layer_index}.attention_norm.weight" for layer_index in range(len(model.layers))},
         **{f"model.layers.{l}.self_attn.{x}_proj.weight": f"layers.{l}.attention.w{x}.weight" for x in ["q", "k", "v", "o"] for l in range(len(model.layers))},
         **{f"model.layers.{l}.post_attention_layernorm.weight": f"layers.{l}.ffn_norm.weight" for l in range(len(model.layers))},
-        **{f"model.layers.{layer_index}.mlp.{x}_proj.weight": f"layers.{layer_index}.feed_forward.w{y}.weight" for x, y in {"gate": "1", "down": "2", "up": "3"}.items() for layer_index in range(len(model.layers))},
+        **{f"model.layers.{layer_index}.mlp.{x}_proj.weight": f"layers.{layer_index}.feed_forward.w{y}.weight"
+           for x, y in {"gate": "1", "down": "2", "up": "3"}.items()
+           for layer_index in range(len(model.layers))},
         "model.norm.weight": "norm.weight",
         "lm_head.weight": "output.weight",
     }

app/models/llama/weights/pull_llama.sh

@@ -1,3 +1,5 @@
+#!/bin/bash
+
 PRESIGNED_URL="https://agi.gpt4.org/llama/LLaMA/*"
 
 # ,30B,65B
@@ -6,6 +8,16 @@ TARGET_FOLDER="./"             # where all files should end up
 
 declare -A N_SHARD_DICT
 
+N_SHARD_DICT["7B"]="0"
+N_SHARD_DICT["13B"]="1"
+N_SHARD_DICT["30B"]="3"
+
+# ,30B,65B
+MODEL_SIZE="7B,13B"  # edit this list with the model sizes you wish to download
+TARGET_FOLDER="./"             # where all files should end up
+
+declare -A N_SHARD_DICT
+
 N_SHARD_DICT["7B"]="0"
 N_SHARD_DICT["13B"]="1"
 N_SHARD_DICT["30B"]="3"
@@ -23,7 +35,7 @@ do
     mkdir -p ${TARGET_FOLDER}"/${i}"
     for s in $(seq -f "0%g" 0 ${N_SHARD_DICT[$i]})
     do
-        wget ${PRESIGNED_URL/'*'/"${i}/consolidated.${s}.pth"} -O ${TARGET_FOLDER}"/${i}/consolidated.${s}.pth"
+        wget "${PRESIGNED_URL/'*'/"${i}/consolidated.${s}.pth"}" -O "${TARGET_FOLDER}/${i}/consolidated.${s}.pth"
     done
     wget ${PRESIGNED_URL/'*'/"${i}/params.json"} -O ${TARGET_FOLDER}"/${i}/params.json"
     wget ${PRESIGNED_URL/'*'/"${i}/checklist.chk"} -O ${TARGET_FOLDER}"/${i}/checklist.chk"