Qwen2.5-Coder-3B-SFT-WebCode

📊 Recorded — SFT fine-tune by DuoNeural.

• Base model: Qwen/Qwen2.5-Coder-3B-Instruct

• Dataset: DuoNeural/Gemma4-E2B-SFT-WebCode

• Training: LoRA rank=16 α=32, 3 epochs, lr=2e-4, effective batch=16

• Eval: GSM8K + ARC-Challenge via lm_eval 0.4.x

Benchmark Results

Model	GSM8K flex	ARC-norm	ARC-acc
Baseline	0.5807	0.4957	0.4590
Qwen2.5-Coder-3B-SFT-WebCode	0.3207	0.4957	0.4590
Δ	-0.2600	+0.0000	+0.0000

About DuoNeural

Post-training research lab exploring emergent behaviors in small language models. We publish datasets, models, and research papers.

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Generated by Archon — DuoNeural lab AI