aliRafik/Qwen2.5_7B_Thinking_alpaca_Clean_16bit
aliRafik/Qwen2.5_7B_Thinking_alpaca_Clean_16bit is a 7.6 billion parameter Qwen2.5 model, fine-tuned by aliRafik on the 52K example yahma/alpaca-cleaned dataset. This 16-bit precision model, optimized with LoRA and Unsloth, excels at instruction following, structured reasoning, and generating clear, complete responses. It is specifically designed to improve the model's ability to process and logically organize multi-step tasks.
Loading preview...
Model Overview
aliRafik/Qwen2.5_7B_Thinking_alpaca_Clean_16bit is a 7.6 billion parameter Qwen2.5 model, fine-tuned by aliRafik using LoRA with Unsloth. The model leverages the yahma/alpaca-cleaned dataset, comprising approximately 52,000 instruction-following examples, to enhance its ability to understand and execute complex instructions. This 16-bit precision model focuses on improving instruction following, structured reasoning, and the clarity and completeness of its outputs.
Key Capabilities
- Enhanced Instruction Following: Better alignment between user requests and generated responses.
- Structured Reasoning: Produces more organized and logical progressions in problem-solving.
- Improved Completeness: More consistently finishes all parts of multi-part instructions.
- Mathematical Consistency: Reduces the likelihood of inconsistent intermediate statements in solutions.
- Clearer Outputs: Generates more readable and practically useful explanations.
Use Cases
This model is particularly well-suited for applications requiring:
- Complex Instruction Execution: Tasks that demand precise adherence to multi-step instructions.
- Structured Problem Solving: Scenarios where logical, step-by-step reasoning is crucial, such as mathematical word problems.
- Generating Coherent Explanations: Producing clear, well-organized, and complete answers to queries.
Qualitative evaluation shows significant improvement in handling work-rate problems, demonstrating a more stable and mathematically sound approach compared to the base model. While not a formal benchmark, this highlights the model's enhanced ability to apply its knowledge effectively and produce useful, consistent outputs.