boradorish/qwen2.5-3b-sft
The boradorish/qwen2.5-3b-sft model is a 3.1 billion parameter language model, fine-tuned from Qwen/Qwen2.5-3B. This model has been specifically fine-tuned on the sunny_reasoning dataset, suggesting an optimization for reasoning tasks. It features a substantial context length of 32768 tokens, making it suitable for applications requiring extensive contextual understanding. Its primary strength lies in its specialized training for reasoning, differentiating it from general-purpose LLMs.
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boradorish/qwen2.5-3b-sft: A Reasoning-Optimized Qwen2.5 Variant
This model, boradorish/qwen2.5-3b-sft, is a specialized fine-tuned version of the 3.1 billion parameter Qwen/Qwen2.5-3B base model. It has undergone Supervised Fine-Tuning (SFT) specifically on the sunny_reasoning dataset.
Key Characteristics & Training:
- Base Model: Qwen/Qwen2.5-3B, a 3.1 billion parameter causal language model.
- Fine-tuning Focus: Optimized for reasoning tasks through training on the
sunny_reasoningdataset. - Context Length: Supports a substantial context window of 32768 tokens, enabling processing of longer inputs.
- Training Hyperparameters: Utilized a learning rate of 4e-05, a total batch size of 64 (with gradient accumulation), and trained for 3 epochs using an AdamW optimizer with a cosine learning rate scheduler.
Potential Use Cases:
Given its fine-tuning on a reasoning-specific dataset, this model is likely best suited for:
- Applications requiring logical deduction or problem-solving.
- Tasks that benefit from understanding and generating reasoned responses.
- Scenarios where a compact yet capable model for reasoning is preferred over larger, more general-purpose alternatives.