MSuryakumar/kuralmind-qwen2.5-3b
MSuryakumar/kuralmind-qwen2.5-3b is a 3.1 billion parameter Qwen2.5 model developed by MSuryakumar. This model was fine-tuned using Unsloth and Hugging Face's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging the Qwen2.5 architecture for efficient performance. The model has a context length of 32768 tokens, making it suitable for processing longer inputs.
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Model Overview
MSuryakumar/kuralmind-qwen2.5-3b is a 3.1 billion parameter language model developed by MSuryakumar. It is based on the Qwen2.5 architecture and was fine-tuned from unsloth/Qwen2.5-3B-Instruct-bnb-4bit. This model benefits from an optimized training process, having been trained 2x faster using the Unsloth library in conjunction with Hugging Face's TRL library.
Key Characteristics
- Architecture: Qwen2.5-3B, a powerful base model known for its capabilities.
- Parameter Count: 3.1 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing of longer texts and more complex queries.
- Training Efficiency: Utilizes Unsloth for accelerated fine-tuning, making it a potentially more cost-effective and faster-to-deploy solution.
Use Cases
This model is suitable for a variety of instruction-following tasks where a compact yet capable language model is required. Its efficient training process suggests it could be a good candidate for applications needing rapid iteration or deployment on resource-constrained environments. The extended context length also makes it versatile for tasks involving longer documents or conversations.