---
license: apache-2.0
base_model: unsloth/Qwen2.5-0.5B-Instruct
tags:
  - function-calling
  - tool-use
  - qwen2.5
  - unsloth
  - lora
  - json-generation
datasets:
  - Salesforce/xlam-function-calling-60k
language:
  - en
pipeline_tag: text-generation
---

# qwen2.5-0.5b-funccall

A fine-tuned `Qwen2.5-0.5B-Instruct` that takes a user query plus a set of available tool/function schemas and outputs the correct function call(s) as clean, parseable JSON — no prose, no markdown fences. Trained as a cheap, accurate "router" model: given a natural-language request and a list of tools, it picks the right tool and fills in arguments correctly, so you don't need to call a much larger model on every turn.

## Model details

- **Base model:** `unsloth/Qwen2.5-0.5B-Instruct`
- **Method:** LoRA fine-tuning via [Unsloth](https://github.com/unslothai/unsloth), merged into the base weights
- **Training data:** [`Salesforce/xlam-function-calling-60k`](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) — 60k function-calling examples, each verified through format checking, real function execution, and semantic verification
- **Task framing:** system message lists available tools as JSON → user message is the natural-language query → assistant response is a JSON array of `{"name": ..., "arguments": ...}` objects, and only that

## Intended use

Drop-in tool/function router for agent loops, CLI dispatchers, or any system that needs to map a user request to a structured function call without paying for a large general-purpose model on every request.

## Why this model

Salesforce's own `xLAM-1b-fc-r` already showed that a sub-2B model can place competitively on the [Berkeley Function-Calling Leaderboard (BFCL)](https://gorilla.cs.berkeley.edu/leaderboard.html), outperforming several much larger general-purpose models. This model explores the same idea at an even smaller scale (0.5B), using Unsloth for fast LoRA fine-tuning.

## Evaluation status

**Not yet evaluated.** Internal exact-match scoring (function name + argument match on a held-out split of the training data) is in progress. The model has **not yet been benchmarked against `Salesforce/xLAM-1b-fc-r`** or against larger zero-shot baselines (e.g. `Qwen2.5-7B-Instruct`) on the real BFCL harness. Numbers below will be filled in once that's run — treat any claims of "matching" or "beating" larger models as **not yet verified** until this section is updated.

| Model | BFCL category | Accuracy |
|---|---|---|
| `nakue/qwen2.5-0.5b-funccall` (this model) | `simple`, `multiple` | *pending* |
| `Qwen2.5-7B-Instruct` (zero-shot) | `simple`, `multiple` | *pending* |
| `Salesforce/xLAM-1b-fc-r` | `simple`, `multiple` | *pending* |

## How to use

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

def build_xlam_system_prompt(tools_xlam_format):
    """
    tools_xlam_format: a list of tool dicts already in xLAM-native shape, e.g.

    [
        {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "location": {
                    "description": "The city to get weather for",
                    "type": "str"
                }
            }
        }
    ]
    """
    return (
        "You are a function-calling assistant. Given a user query and a list of "
        "available tools, respond with ONLY a JSON array of the function call(s) "
        "needed to fulfill the query. Each item must have 'name' and 'arguments' "
        "keys. Do not include any explanation, markdown formatting, or text other "
        f"than the raw JSON array.\n\nAvailable tools:\n{json.dumps(tools_xlam_format, indent=2)}"
    )


# Example usage with your weather tool, written directly in xLAM format:
tools = [
    {
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
            "location": {
                "description": "The city to get weather for",
                "type": "str"
            }
        }
    }
]

system_msg = build_xlam_system_prompt(tools)

messages = [
    {"role": "system", "content": system_msg},
    {"role": "user", "content": "What's the weather like in Harare right now?"},
]

inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
).to(model.device)

out = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=False,
    pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)
```

## Limitations

- Trained only on `simple` and `multiple`-style single-turn function calling. Not trained or tested on multi-turn conversations, parallel calls, or queries with no matching tool (irrelevance detection).
- Output is sensitive to tool-schema formatting; large or unusual schemas outside the training distribution may degrade reliability.
- Evaluation against published baselines is pending — see above.

## Training details

Fine-tuned with Unsloth's LoRA implementation (`r=16`, `lora_alpha=16`, targeting attention and MLP projection layers), 2 epochs, cosine LR schedule, on a held-out-respecting split of `xlam-function-calling-60k` (500 examples reserved for test, 300 for validation, remainder for training).

## Citation

If you use this model, please also cite the underlying dataset:

```
@misc{xlam,
  title={xLAM: A Family of Large Action Models to Empower AI Agent Systems},
  author={Salesforce AI Research},
  year={2024}
}
```