YumengWithG/Qwen3.5-9B-meSFT-gsm8k-noCoT-SFT
YumengWithG/Qwen3.5-9B-meSFT-gsm8k-noCoT-SFT is a 9 billion parameter Qwen3.5-based model, fine-tuned specifically for mathematical reasoning on the GSM8K dataset. This model is optimized to provide direct answers to math problems without generating chain-of-thought explanations. It leverages Qwen3.5's multimodal conditional-generation architecture, making it suitable for focused numerical problem-solving tasks.
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Overview
This model, YumengWithG/Qwen3.5-9B-meSFT-gsm8k-noCoT-SFT, is a specialized supervised fine-tune of zhengbang0707/Qwen3.5-9B-meSFT. Its primary focus is on mathematical reasoning, specifically trained on the GSM8K dataset to produce final answers without intermediate chain-of-thought (CoT) steps.
Key Capabilities
- Direct Mathematical Problem Solving: Optimized to provide concise, final answers for arithmetic and word problems, bypassing detailed reasoning steps.
- Qwen3.5 Architecture: Built upon the Qwen3.5 multimodal conditional-generation architecture, utilizing its native chat template.
- Efficient Inference: Configured for no-CoT inference by setting
enable_thinking=Falsein the processor.
Training Details
- Dataset: Fine-tuned using the
openai/gsm8kdataset's main training split. - Parameters: Trained with BF16 parameters over 3 epochs using 4-GPU FSDP.
- Performance: Achieved a final training loss of 0.4120 and a validation loss of 0.7454. Judge evaluation on 1319 examples showed 37.38% correctness using
us.anthropic.claude-sonnet-4-6as the judge model.
When to Use This Model
This model is ideal for applications requiring quick, direct numerical answers from mathematical problems, particularly when explicit step-by-step reasoning is not needed or desired. It's suitable for integration into systems where only the final numerical output is consumed.