zeeshaan-ai/solo-tune-test684
The zeeshaan-ai/solo-tune-test684 is a 0.8 billion parameter language model, fine-tuned from Qwen/Qwen3-0.6B using LoRA (PEFT) for 2 epochs. This model is specifically optimized for mathematical reasoning and problem-solving, leveraging the openai/gsm8k dataset. Its compact size and specialized training make it suitable for efficient deployment in applications requiring arithmetic and logical deduction.
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Model Overview
The zeeshaan-ai/solo-tune-test684 is a compact 0.8 billion parameter language model, derived from the Qwen/Qwen3-0.6B base model. It has been fine-tuned using the LoRA (Low-Rank Adaptation) method, a parameter-efficient fine-tuning technique, over 2 epochs with a maximum of 100 steps.
Key Capabilities
- Mathematical Reasoning: The model's training on the
openai/gsm8kdataset, which focuses on grade school math problems, indicates a specialization in arithmetic and logical problem-solving. - Efficiency: With 0.8 billion parameters, it offers a balance between performance and computational efficiency, making it suitable for resource-constrained environments.
- LoRA Fine-tuning: The use of LoRA (r=4, alpha=4) allows for efficient adaptation of the base model to specific tasks without requiring extensive computational resources for full model fine-tuning.
Training Details
The model was trained with a batch size of 2, gradient accumulation of 4, and a learning rate of 0.0002. It processed sequences up to a maximum length of 2048 tokens, with the entire training process completing in approximately 3 minutes.
Good For
- Applications requiring mathematical problem-solving or numerical reasoning.
- Scenarios where a lightweight yet capable model for specific tasks is preferred over larger, more general-purpose LLMs.
- Integration into systems that benefit from efficient fine-tuning and deployment.