README.md

---
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- function-calling
- llama3.2
- fine-tuned
- lora
language:
- en
---

# Llama 3.2 3B Function Calling Model

This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) for function calling tasks.

## Model Details

- **Base Model**: Llama 3.2 3B Instruct
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Dataset**: Salesforce/xlam-function-calling-60k (1000 samples)
- **Training**: 2 epochs with learning rate 2e-5

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("TurkishCodeMan/llama3.2-3b-intruct-function-calling")
tokenizer = AutoTokenizer.from_pretrained("TurkishCodeMan/llama3.2-3b-intruct-function-calling")

prompt = '''<|system|>
Available functions:
- get_weather: Gets current weather for a location

GPT 4 Correct user:
<|user|>
What's the weather in Tokyo?
GPT 4 correct assistant:'''

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Details

- **Learning Rate**: 2e-5
- **Batch Size**: 2 (per device)
- **Gradient Accumulation**: 8 steps
- **LoRA Rank**: 8
- **LoRA Alpha**: 16
- **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

## Performance

The model demonstrates excellent function calling capabilities:
- Correct function selection
- Proper argument formatting
- Professional response structure