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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)) |