---
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- function-calling
- tool-use
- peft
- lora
- qwen2.5
datasets:
- Salesforce/xlam-function-calling-60k
language:
- en
---

# Qwen2.5-0.5B Function Calling (Fine-tuned)

A lightweight function-calling model fine-tuned from 
[Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) 
on the [Salesforce/xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) dataset.

Designed for precise, structured JSON function call generation in 
resource-constrained environments.

## Training details
- **Base model**: Qwen2.5-0.5B-Instruct
- **Method**: LoRA (r=16, alpha=32)
- **Target modules**: q_proj, v_proj, k_proj, o_proj
- **Training samples**: 1500
- **Steps**: 150
- **Precision**: fp16

## Usage

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

model_id = "silvermete0r/qwen2.5-nano-function-master"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

tools = [{"name": "get_weather", "description": "Get weather", "parameters": {"location": {"type": "string"}}}]

messages = [
    {"role": "system", "content": f"You have access to:\n{json.dumps(tools)}\nRespond strictly with a JSON array of function calls."},
    {"role": "user",   "content": "What's the weather in Paris?"},
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=128, do_sample=False)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
```

## Benchmark results (100-sample (random) test split)

```
Metric                        Before      After      Delta
---------------------------------------------------------
json_valid %                    41.0       96.0      +55.0
name_match %                    24.0       94.0      +70.0
args_keys_match %               14.0       79.0      +65.0
args_exact %                    10.0       69.0      +59.0
```