README.md

metadata

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 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

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