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Balasandhya/
llm-tool-call-lora-Qwen0.5B

Name: llm-tool-call-lora-Qwen0.5B
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🛠️ Qwen 2.5-0.5B Tool Call Fine-Tuning (Phase 1 — Synthetic SFT)

Fine-tuning Qwen 2.5-0.5B on a small synthetic dataset to make structured JSON tool calls using QLoRA.

This is Phase 1 of a 3-phase experiment. See the full experiment repo for SFT + GRPO on real data.

Trains a 0.5B model to respond to natural language with structured tool calls:

{"tool": "get_stock_price", "arguments": {"ticker": "MSFT"}}


Model	Qwen/Qwen2.5-0.5B-Instruct
Method	QLoRA (4-bit, LoRA rank 16)
Dataset	14 hand-written synthetic examples
Tools	get_weather, search_web, calculator, get_stock_price
Platform	Google Colab (T4 GPU)

Evaluated on 10 queries (5 seen during training, 5 unseen):

Key observations:

Model learned the concept of JSON tool calls but hallucinated tool names ("weather" instead of "get_weather")
Wrong argument keys ("ticker_symbol" instead of "ticker")
Calculator answered directly in plain text instead of calling the tool
14 examples is too small — model memorises poorly and doesn't generalise

File	Description
`qwen25_tool_call_finetune.py`	Training script — SFT with synthetic data
`Qwen2.5-0.5B_tool_call_finetune_eval.py`	Evaluation script — scores model on 10 queries

14 examples is not enough — model fails to generalise even to similar queries
Exact argument matching is strict — "ticker_symbol" vs "ticker" counts as failure
Bigger dataset + RL needed → See Phase 2 & 3 for improvement

Qwen2.5-0.5B • QLoRA • SFT • trl • peft • Google Colab T4