metadata
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- function-calling
- tool-use
- fine-tuned
- llama
datasets:
- Salesforce/xlam-function-calling-60k
pipeline_tag: text-generation
Llama 3.1 8B Function Calling
Fine-tuned Llama 3.1 8B Instruct for function/tool calling.
Training
- Dataset: 900 examples from Salesforce/xlam-function-calling-60k
- Method: LoRA
- Trainable params: 42M / 8B (0.52%)
- Epochs: 1
- Loss: 0.66 → 0.63
Evaluation (100 held-out samples)
- Exact match: 62%
- Function name accuracy: ~90%+
Usage
from vllm import LLM
llm = LLM(model="alfazick/llama-3.1-8b-function-calling")
Or with transformers:
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("alfazick/llama-3.1-8b-function-calling")
tokenizer = AutoTokenizer.from_pretrained("alfazick/llama-3.1-8b-function-calling")
Prompt Format
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful assistant with access to the following tools or function calls. Your task is to produce a sequence of tools or function calls necessary to generate response to the user utterance. Use the following tools or function calls as required:
[{"name": "func_name", "description": "...", "parameters": {...}}]<|eot_id|><|start_header_id|>user<|end_header_id|>
{query}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Output Format
[{"name": "function_name", "arguments": {"arg": "value"}}]
Limitations
- Trained on 900 examples (proof of concept)
- May have argument variations vs ground truth
- Best for single/simple tool calls
Training Details
- Framework: Unsloth 2025.11.2 + TRL
- Hardware: RTX 5090 (32GB)
- Method: LoRA (r=16, alpha=16)