Juhaann20/DeepSeek-R1-Distill-Qwen-7B-LoRA-Task
Juhaann20/DeepSeek-R1-Distill-Qwen-7B-LoRA-Task is a 7.6 billion parameter Qwen2-based causal language model developed by Juhaann20. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general language tasks, leveraging its Qwen2 architecture and efficient fine-tuning process.
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Model Overview
Juhaann20/DeepSeek-R1-Distill-Qwen-7B-LoRA-Task is a 7.6 billion parameter language model based on the Qwen2 architecture. It was developed by Juhaann20 and fine-tuned from the unsloth/DeepSeek-R1-Distill-Qwen-7B-bnb-4bit model.
Key Characteristics
- Architecture: Qwen2-based causal language model.
- Parameter Count: 7.6 billion parameters.
- Context Length: Supports a context length of 32768 tokens.
- Efficient Fine-tuning: The model was fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Use Cases
This model is suitable for a variety of general language understanding and generation tasks, benefiting from its Qwen2 foundation and efficient fine-tuning. Its 32K context window makes it capable of processing longer inputs and generating more coherent, extended responses.