wijan/thesis
The wijan/thesis model is a 3.1 billion parameter Qwen2.5-Instruct variant, fine-tuned by wijan. It leverages Unsloth and Huggingface's TRL library for accelerated training, making it a highly efficient and performant model for various instruction-following tasks. This model is optimized for rapid deployment and inference in applications requiring a compact yet capable language model.
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Overview
wijan/thesis is a 3.1 billion parameter instruction-tuned language model, developed by wijan. It is a fine-tuned version of the unsloth/Qwen2.5-3B-Instruct-unsloth-bnb-4bit model, utilizing the Unsloth library and Huggingface's TRL for its training process. This approach enabled a significantly faster training cycle, making it an efficient choice for developers.
Key Characteristics
- Base Model: Qwen2.5-3B-Instruct architecture.
- Parameter Count: 3.1 billion parameters.
- Context Length: Supports a context length of 32768 tokens.
- Training Efficiency: Benefited from 2x faster training using Unsloth, indicating optimizations for resource-effective fine-tuning.
- License: Released under the permissive Apache-2.0 license, allowing for broad use and distribution.
Good For
- Instruction Following: Designed to excel at tasks requiring adherence to specific instructions.
- Efficient Deployment: Its compact size and optimized training suggest suitability for applications where computational resources are a consideration.
- Rapid Prototyping: The accelerated training process makes it a good candidate for quick experimentation and development cycles.