ZhuofengLi/tool-n1-reason-lora-sft-800-step
ZhuofengLi/tool-n1-reason-lora-sft-800-step is a 7.6 billion parameter language model fine-tuned from Qwen2.5-7B-Instruct. This model has undergone LoRA SFT training for 800 steps using the Tool-N1 dataset. It is specifically optimized for reasoning tasks, leveraging its instruction-following base model and specialized training data.
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Model Overview
This model, developed by ZhuofengLi, is a 7.6 billion parameter language model based on the Qwen2.5-7B-Instruct architecture. It has been fine-tuned using Low-Rank Adaptation (LoRA) with 800 training steps on the Tool-N1 dataset. The primary focus of this fine-tuning is to enhance the model's reasoning capabilities, building upon the strong instruction-following foundation of its base model.
Key Characteristics
- Base Model: Qwen2.5-7B-Instruct, providing a robust foundation for instruction following.
- Parameter Count: 7.6 billion parameters, offering a balance between performance and computational efficiency.
- Training Method: LoRA Supervised Fine-Tuning (SFT) for efficient adaptation.
- Training Data: Utilizes the Tool-N1 dataset, indicating a specialization in tasks related to tool use or complex reasoning.
- Context Length: Supports a substantial context window of 32768 tokens.
Intended Use Cases
While specific direct use cases are not detailed in the model card, its training on the Tool-N1 dataset and focus on reasoning suggest suitability for applications requiring:
- Complex problem-solving.
- Logical deduction and inference.
- Tasks that might involve understanding and applying external tools or functions.