| --- |
| license: llama3.1 |
| language: |
| - en |
| - py |
| library_name: transformers |
| tags: |
| - llama-3.1 |
| - python |
| - code-generation |
| - instruction-following |
| - fine-tune |
| - alpaca |
| - unsloth |
| base_model: meta-llama/Meta-Llama-3.1-8B-Instruct |
| datasets: |
| - iamtarun/python_code_instructions_18k_alpaca |
| --- |
| --- |
|
|
| # Llama-3.1-8B-Instruct-Python-Alpaca-Unsloth |
|
|
| This is a fine-tuned version of Meta's **`Llama-3.1-8B-Instruct`** model, specialized for Python code generation. It was trained on the high-quality **`iamtarun/python_code_instructions_18k_alpaca`** dataset using the **Unsloth** library for significantly faster training and reduced memory usage. |
|
|
| The result is a powerful and responsive coding assistant, designed to follow instructions and generate accurate, high-quality Python code. |
|
|
| --- |
| ## ## Model Details 🛠️ |
|
|
| * **Base Model:** `meta-llama/Meta-Llama-3.1-8B-Instruct` |
| * **Dataset:** `iamtarun/python_code_instructions_18k_alpaca` (18,000 instruction-following examples for Python) |
| * **Fine-tuning Technique:** QLoRA (4-bit Quantization with LoRA adapters) |
| * **Framework:** Unsloth (for up to 2x faster training and optimized memory) |
|
|
| --- |
| ## ## How to Use 👨💻 |
|
|
| This model is designed to be used with the Unsloth library for maximum performance, but it can also be used with the standard Hugging Face `transformers` library. For the best results, always use the Llama 3 chat template. |
|
|
| ### ### Using with Unsloth (Recommended) |
|
|
| ```python |
| from unsloth import FastLanguageModel |
| import torch |
| |
| model, tokenizer = FastLanguageModel.from_pretrained( |
| model_name = "YOUR_USERNAME/YOUR_MODEL_NAME", # REMEMBER TO REPLACE THIS |
| max_seq_length = 4096, |
| dtype = None, |
| load_in_4bit = True, |
| ) |
| |
| # Prepare the model for faster inference |
| FastLanguageModel.for_inference(model) |
| |
| messages = [ |
| { |
| "role": "system", |
| "content": "You are a helpful Python coding assistant. Please provide a clear, concise, and correct Python code response to the user's request." |
| }, |
| { |
| "role": "user", |
| "content": "Create a Python function that finds the nth Fibonacci number using recursion." |
| }, |
| ] |
| |
| input_ids = tokenizer.apply_chat_template( |
| messages, |
| add_generation_prompt=True, |
| return_tensors="pt" |
| ).to(model.device) |
| |
| outputs = model.generate( |
| input_ids, |
| max_new_tokens=200, |
| do_sample=True, |
| temperature=0.6, |
| top_p=0.9, |
| eos_token_id=tokenizer.eos_token_id |
| ) |
| |
| response = outputs[0][input_ids.shape[-1]:] |
| print(tokenizer.decode(response, skip_special_tokens=True)) |
