devpotatopotato/qwen3-8b-sft-260901-acereason-bigmath
devpotatopotato/qwen3-8b-sft-260901-acereason-bigmath is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B with a 32768 token context length. This model is specifically optimized for enhanced reasoning and mathematical tasks, having been fine-tuned on the acereason_keyword_details and bigmath_keyword_details datasets. It is designed to improve performance in complex problem-solving and numerical operations.
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Model Overview
The devpotatopotato/qwen3-8b-sft-260901-acereason-bigmath model is an 8 billion parameter language model, fine-tuned from the base Qwen/Qwen3-8B architecture. It features a substantial context length of 32768 tokens, making it suitable for processing longer inputs and maintaining conversational coherence over extended interactions.
Key Capabilities
- Enhanced Reasoning: The model has undergone specific fine-tuning on the
acereason_keyword_detailsdataset, aiming to improve its logical reasoning abilities. - Mathematical Proficiency: Further fine-tuning on the
bigmath_keyword_detailsdataset targets increased accuracy and capability in mathematical problem-solving and numerical tasks. - Qwen3-8B Foundation: Leverages the robust architecture and general language understanding of the Qwen3-8B base model.
Training Details
The fine-tuning process utilized the following key hyperparameters:
- Learning Rate: 4e-05
- Batch Size: A total training batch size of 128 (8 per device with 8 gradient accumulation steps).
- Optimizer: AdamW with betas=(0.9, 0.999) and epsilon=1e-08.
- Epochs: Trained for 5.0 epochs.
- Scheduler: Cosine learning rate scheduler with a 0.05 warmup ratio.
Good For
- Applications requiring strong logical reasoning.
- Tasks involving complex mathematical computations or problem-solving.
- Use cases where a balance between model size and specialized reasoning/math capabilities is desired.