Create README.md

README.md

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+---
+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))