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Model: nakue/qwen2.5-0.5b-funccall Source: Original Platform
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README.md
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README.md
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---
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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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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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58
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": null,
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||||
"torch_dtype": "bfloat16",
|
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"eos_token_id": 151645,
|
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"hidden_act": "silu",
|
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"hidden_size": 896,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 4864,
|
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"layer_types": [
|
||||
"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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|
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|
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|
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|
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"full_attention",
|
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|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 21,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 14,
|
||||
"num_hidden_layers": 24,
|
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"num_key_value_heads": 2,
|
||||
"pad_token_id": 151665,
|
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"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 1000000.0,
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"rope_type": "default"
|
||||
},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"unsloth_fixed": true,
|
||||
"unsloth_version": "2026.6.8",
|
||||
"use_cache": false,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
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generation_config.json
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generation_config.json
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{
|
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"bos_token_id": 151643,
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"do_sample": true,
|
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|
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"max_length": 32768,
|
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"pad_token_id": 151665,
|
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"repetition_penalty": 1.1,
|
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"temperature": 0.7,
|
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"top_k": 20,
|
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"top_p": 0.8,
|
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"transformers_version": "5.5.0"
|
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}
|
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3
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:8aa1650d62f4473455c50f5bbacf5064c5e2ae8e2c54445247778142d4bbc8e7
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size 988097824
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3
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
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size 11422356
|
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202
tokenizer_config.json
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tokenizer_config.json
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{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"is_local": false,
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|PAD_TOKEN|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
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|
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|
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|
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|
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|
||||
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||||
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|
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|
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|
||||
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|
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|
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|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<|PAD_TOKEN|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
|
||||
}
|
||||
Reference in New Issue
Block a user