|
|
| --- |
| 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 |
| |
