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Ayush0110
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Model Details
Model Provider
Ayush0110
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Routes a query to one (or several) of these tools, or to a direct answer:
web_search, calculator, weather, wikipedia, datetime,
dictionary, translate, unit_converter, web_reader
— plus no_tool (answer directly) and multi_tool (chained calls).
Output format:
text
<tool_calls>[{"name": "weather", "arguments": {"location": "Tokyo"}}]</tool_calls>Measured on a hand-written, non-circular test set (36 realistic, indirectly phrased queries, hand-labeled — no teacher model involved), comparing the base model against this adapter on identical inputs. Grading is format-agnostic: a prediction counts if the correct tool is identified in any recognizable format, so the base model isn't penalized for not using the trained format.
| Model | Routing accuracy | Strict-format accuracy |
|---|---|---|
| Base Qwen2.5-7B-Instruct | 75.0% | 75.0% |
| ToolForge (this adapter) | 83.3% | 83.3% |
| Gain from fine-tuning | +8.3 pp | +8.3 pp |
Key point: strict and lenient scores are identical for both models — base
Qwen already emits parseable tool-call formats, so the improvement comes from
better routing decisions, not output formatting. Gains concentrate on
disambiguating web_search vs wikipedia, unit_converter vs calculator,
and multi-tool selection.
A separate ablation on a held-out split of the (teacher-labeled) synthetic data reports ~86%, but that number is partly circular and is best read as an internal hyperparameter comparison. The table above is the unbiased estimate.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerimport torch base_id = "Qwen/Qwen2.5-7B-Instruct"tok = AutoTokenizer.from_pretrained(base_id)base = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(base, "Ayush0110/toolforge-qwen7b-r64")model.eval() SYS = ("You are a tool-routing assistant. Given a user query, decide which tool(s) " "to call and with what arguments. If no tool is needed, respond directly. " "You have access to: web_search, calculator, weather, wikipedia, datetime, " "dictionary, translate, unit_converter, web_reader. " 'Output tool calls as: <tool_calls>[{"name": "tool", "arguments": {...}}]</tool_calls>') msgs = [{"role": "system", "content": SYS}, {"role": "user", "content": "is it jacket weather in Copenhagen right now"}]text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)inputs = tok(text, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=128, do_sample=False)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))# -> <tool_calls>[{"name": "weather", "arguments": {"location": "Copenhagen"}}]</tool_calls>| Base | Qwen/Qwen2.5-7B-Instruct |
| Quantization | 4-bit NF4 + double quant |
| LoRA | r=64, α=128, dropout=0.05, targets: q,k,v,o,gate,up,down |
| Optimizer / LR | AdamW, 2e-4 cosine, 10% warmup |
| Batch | 4 × 4 grad-accum = 16 effective |
| Epochs | 3 (best at eval_loss ≈ 0.14) |
| Data | 1,173 examples (template-generated + Gemini-2.5-flash distilled) |
| Hardware | single T4 (16GB), ~2.4 h |
| Tracking |
Apache-2.0 (inherits from the Qwen2.5 base model).
| Weights & Biases |