135 lines
5.5 KiB
Markdown
135 lines
5.5 KiB
Markdown
---
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license: apache-2.0
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base_model: unsloth/Qwen2.5-0.5B-Instruct
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tags:
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- function-calling
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- tool-use
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- qwen2.5
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- unsloth
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- lora
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- json-generation
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datasets:
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- Salesforce/xlam-function-calling-60k
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language:
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- en
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pipeline_tag: text-generation
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---
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# qwen2.5-0.5b-funccall
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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.
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## Model details
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- **Base model:** `unsloth/Qwen2.5-0.5B-Instruct`
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- **Method:** LoRA fine-tuning via [Unsloth](https://github.com/unslothai/unsloth), merged into the base weights
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- **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
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- **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
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## Intended use
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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.
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## Why this model
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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.
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## Evaluation status
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**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.
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| Model | BFCL category | Accuracy |
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|---|---|---|
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| `nakue/qwen2.5-0.5b-funccall` (this model) | `simple`, `multiple` | *pending* |
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| `Qwen2.5-7B-Instruct` (zero-shot) | `simple`, `multiple` | *pending* |
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| `Salesforce/xLAM-1b-fc-r` | `simple`, `multiple` | *pending* |
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## How to use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch, json
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def build_xlam_system_prompt(tools_xlam_format):
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"""
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tools_xlam_format: a list of tool dicts already in xLAM-native shape, e.g.
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[
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{
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"name": "get_weather",
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"description": "Get current weather for a location",
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"parameters": {
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"location": {
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"description": "The city to get weather for",
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"type": "str"
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}
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}
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}
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]
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"""
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return (
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"You are a function-calling assistant. Given a user query and a list of "
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"available tools, respond with ONLY a JSON array of the function call(s) "
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"needed to fulfill the query. Each item must have 'name' and 'arguments' "
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"keys. Do not include any explanation, markdown formatting, or text other "
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f"than the raw JSON array.\n\nAvailable tools:\n{json.dumps(tools_xlam_format, indent=2)}"
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)
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# Example usage with your weather tool, written directly in xLAM format:
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tools = [
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{
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"name": "get_weather",
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"description": "Get current weather for a location",
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"parameters": {
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"location": {
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"description": "The city to get weather for",
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"type": "str"
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}
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}
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}
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]
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system_msg = build_xlam_system_prompt(tools)
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messages = [
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{"role": "system", "content": system_msg},
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{"role": "user", "content": "What's the weather like in Harare right now?"},
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]
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inputs = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
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).to(model.device)
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out = model.generate(
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**inputs,
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max_new_tokens=256,
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do_sample=False,
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pad_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
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print(response)
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```
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## Limitations
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- 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).
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- Output is sensitive to tool-schema formatting; large or unusual schemas outside the training distribution may degrade reliability.
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- Evaluation against published baselines is pending — see above.
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## Training details
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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).
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## Citation
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If you use this model, please also cite the underlying dataset:
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```
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@misc{xlam,
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title={xLAM: A Family of Large Action Models to Empower AI Agent Systems},
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author={Salesforce AI Research},
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year={2024}
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}
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```
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