style: apply pre-commit formatting fixes

- Fix trailing whitespace and end-of-file issues across docs/
- Apply black, isort, and autoflake formatting to Python files
- Add CI workflow and pytest configuration
- Standardize code formatting across entire codebase

These are automated formatting changes from pre-commit hooks to ensure
consistent code style and documentation formatting.

Author: glennko

.github/stochastic_logo_dark.svg

@@ -0,0 +1,32 @@
+name: CI
+
+on:
+  push:
+    branches: [ main, master, dev ]
+  pull_request:
+    branches: [ main, master, dev ]
+
+jobs:
+  pre-commit:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+
+      - name: Install pre-commit
+        run: |
+          python -m pip install --upgrade pip
+          pip install pre-commit
+
+      - name: Run pre-commit
+        run: |
+          pre-commit run -a --show-diff-on-failure
+
+      - name: Verify key files present
+        run: |
+          test -f AGENTS.md

.github/stochastic_logo_light.svg

@@ -9,4 +9,4 @@ import DocCardList from '@theme/DocCardList';
 
 # 🧗🏻 Advanced Topics
 
-<DocCardList />
+<DocCardList />

.github/workflows/ci.yml

@@ -75,4 +75,4 @@ Ensure that the model path you provide is a directory containing a valid xturing
   }
   ```
 
-By following these steps, you can effectively run your fine-tuned model for text generation through the FastAPI server, facilitating seamless inference with structured requests and responses.
+By following these steps, you can effectively run your fine-tuned model for text generation through the FastAPI server, facilitating seamless inference with structured requests and responses.

docs/docs/advanced/advanced.md

@@ -16,16 +16,16 @@ To generate a dataset we will make use of **engines** that consist of third part
 
   OpenAI api key can be obtained [here](https://beta.openai.com/account/api-keys)
 
-  
+
 ```python
 from xturing.model_apis.openai import ChatGPT, Davinci
 engine = ChatGPT("your-api-key")
 # or
 engine = Davinci("your-api-key")
 ```
-        
+
   </TabItem>
-  
+
   <TabItem value="cohere" label="Cohere">
 
   ```python
@@ -75,7 +75,7 @@ Each line of this file needs to be a _JSON_ object with the following fields:
 | __instances__ |  List[Dict[str,str]] |  A list of input-output pairs that provide examples of what the model should output for this task. Each input-output pair is an object with two fields: __input__ and __output__. |
 | __is_classification__ |  boolean | A flag that indicates whether this is a classification task or not. If this flag is set to true, the output should be a single label (e.g. a category or class), otherwise the output can be any text. The default value is false. |
 
-Here's an example of a task in the above mentioned format: 
+Here's an example of a task in the above mentioned format:
 
 ```json
 {
@@ -100,16 +100,16 @@ Here is how an sample `tasks.jsonl` file should like:
 
 ```json
 {
-  "id": "seed_task_0", 
-  "name": "breakfast_suggestion", 
-  "instruction": "Is there anything I can eat for a breakfast that doesn't include eggs, yet includes protein, and has roughly 700-1000 calories?", 
-  "instances": [{"input": "", "output": "Yes, you can have 1 oatmeal banana protein shake and 4 strips of bacon. The oatmeal banana protein shake may contain 1/2 cup oatmeal, 60 grams whey protein powder, 1/2 medium banana, 1tbsp flaxseed oil and 1/2 cup watter, totalling about 550 calories. The 4 strips of bacon contains about 200 calories."}], 
+  "id": "seed_task_0",
+  "name": "breakfast_suggestion",
+  "instruction": "Is there anything I can eat for a breakfast that doesn't include eggs, yet includes protein, and has roughly 700-1000 calories?",
+  "instances": [{"input": "", "output": "Yes, you can have 1 oatmeal banana protein shake and 4 strips of bacon. The oatmeal banana protein shake may contain 1/2 cup oatmeal, 60 grams whey protein powder, 1/2 medium banana, 1tbsp flaxseed oil and 1/2 cup watter, totalling about 550 calories. The 4 strips of bacon contains about 200 calories."}],
   "is_classification": false
 }
 {
-  "id": "seed_task_1", 
-  "name": "antonym_relation", 
-  "instruction": "What is the relation between the given pairs?", 
+  "id": "seed_task_1",
+  "name": "antonym_relation",
+  "instruction": "What is the relation between the given pairs?",
   "instances": [{"input": "Night : Day :: Right : Left", "output": "The relation between the given pairs is that they are opposites."}], "is_classification": false
 }
 ```
@@ -129,7 +129,7 @@ from xturing.model_apis.openai import Davinci
 ## Load the required engine
 engine = Davinci("your-api-key")
 
-## Generate the dataset 
+## Generate the dataset
 dataset = InstructionDataset.generate_dataset(path="./tasks.jsonl", engine=engine)
 
 ## Save the dataset instance
@@ -162,7 +162,7 @@ A cap on the size of the dataset, this can help to create a more diverse dataset
 
 ## From custom data
 
-We can also generate a dataset from our own files. 
+We can also generate a dataset from our own files.
 
 <details>
   <summary>
@@ -264,4 +264,3 @@ The number of samples that will be generated for each chunk. The default value i
 
 
 <!-- --- -->
-

docs/docs/advanced/api_server.md

@@ -15,7 +15,7 @@ const modelList = {
   cerebras: 'Cerebras',
   distilgpt2: 'DistilGPT-2',
   galactica: 'Galactica',
-  gptj: 'GPT-J', 
+  gptj: 'GPT-J',
   gpt2: 'GPT-2',
   llama: 'LLaMA',
   llama2: 'LLaMA 2',
@@ -35,7 +35,7 @@ export default function FinetuneCode(
   } else {
     finalKey = `${code.model}_${code.technique}`
   }
-  
+
   useEffect(() => {
     setCode({
       model: 'llama',
@@ -87,8 +87,8 @@ export default function FinetuneCode(
 from xturing.datasets import InstructionDataset
 
 instruction_dataset = InstructionDataset("...")
-model = BaseModel.create("${finalKey}")`} 
+model = BaseModel.create("${finalKey}")`}
       />
     </div>
   )
-}
+}

docs/docs/advanced/generate.md

@@ -15,7 +15,7 @@ For advanced usage, you can customize the `finetuning_config` attribute of the m
 In this tutorial, we will be loading one of the [supported models](/overview/supported_models) and customizing it's fine-tune configuration before calibrating the model to the desired dataset.
 
 ### Load the model and the dataset
-First, we need to load the model and the dataset we want to use. 
+First, we need to load the model and the dataset we want to use.
 
 <FinetuneCode />
 
@@ -31,7 +31,7 @@ finetuning_config = model.finetuning_config()
 Print the `finetuning_config` object to check the default configuration.
 
 ### Customize the configuration
-Now, we can customize the generation configuration as we wish. All the customizable parameters are list [below](#parameters). 
+Now, we can customize the generation configuration as we wish. All the customizable parameters are list [below](#parameters).
 
 ```python
 finetuning_config.batch_size = 64
@@ -98,4 +98,3 @@ max_grad_norm: 2.0
 save_total_limit: 4
 optimizer_name: adamw
 output_dir: saved_model -->
-

docs/docs/configuration/fine_tune_code.jsx

@@ -14,7 +14,7 @@ const modelList = {
   cerebras: 'Cerebras',
   distilgpt2: 'DistilGPT-2',
   galactica: 'Galactica',
-  gptj: 'GPT-J', 
+  gptj: 'GPT-J',
   gpt2: 'GPT-2',
   llama: 'LLaMA',
   llama2: 'LLaMA 2',
@@ -34,7 +34,7 @@ export default function InferenceCode(
   } else {
     finalKey = `${code.model}_${code.technique}`
   }
-  
+
   useEffect(() => {
     setCode({
       model: 'llama',
@@ -83,8 +83,8 @@ export default function InferenceCode(
       showLineNumbers={false}
       language='python'
       children={`from xturing.models import BaseModel
-model = BaseModel.create("${finalKey}")`} 
+model = BaseModel.create("${finalKey}")`}
       />
     </div>
   )
-}
+}

docs/docs/configuration/finetune_configure.md

@@ -33,7 +33,7 @@ We can print the `generation_config` object to check the default configuration.
 
 ### Customize the configuration
 
-Now, we can customize the generation configuration as we wish. All the customizable parameters are list [below](#parameters). 
+Now, we can customize the generation configuration as we wish. All the customizable parameters are list [below](#parameters).
 
 ```python
 generation_config.max_new_tokens = 256