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Model: nakue/qwen2.5-0.5b-funccall
Source: Original Platform

.gitattributes

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README.md

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+---
+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}
+}
+```

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": null,
+    "torch_dtype": "bfloat16",
+    "eos_token_id": 151645,
+    "hidden_act": "silu",
+    "hidden_size": 896,
+    "initializer_range": 0.02,
+    "intermediate_size": 4864,
+    "layer_types": [
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 32768,
+    "max_window_layers": 21,
+    "model_type": "qwen2",
+    "num_attention_heads": 14,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 2,
+    "pad_token_id": 151665,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+        "rope_theta": 1000000.0,
+        "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
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 32768,
+  "pad_token_id": 151665,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.5.0"
+}

model.safetensors

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+oid sha256:8aa1650d62f4473455c50f5bbacf5064c5e2ae8e2c54445247778142d4bbc8e7
+size 988097824

tokenizer.json

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+oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
+size 11422356

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,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
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+    "151645": {
+      "content": "<|im_end|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
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+    "151646": {
+      "content": "<|object_ref_start|>",
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+      "special": true
+    },
+    "151647": {
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+    },
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+      "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"
+}