FineTunedQWENoncoding

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This model is a fine-tuned version of unsloth/qwen2.5-coder-1.5b-bnb-4bit, specifically adapted to solve coding problems using the CodeAlpaca-20k dataset. The model has been optimized for generating high-quality solutions to programming questions across various languages. It leverages the benefits of low-bit quantization for efficient inference while maintaining competitive performance.

Model Details

Model Description

Architecture: The model is based on QWen-2.5, a 1.5-billion parameter model optimized using 4-bit quantization via Bits and Bytes. This allows for reduced memory usage and faster inference while maintaining the model’s effectiveness. Fine-tuning Process: The model was fine-tuned on the CodeAlpaca-20k dataset, a large corpus of coding-related prompts and solutions that span multiple programming languages. The goal of the fine-tuning was to improve the model’s ability to solve real-world coding problems and generate accurate, executable code. Max Sequence Length: 2048 tokens to accommodate larger input sizes. Quantization: The use of 4-bit quantization significantly reduces the memory footprint without sacrificing much on model performance, making it ideal for deployment in environments with limited resources

• Developed by: Bidhan Acharya
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• Finetuned from model [optional]: Qwen/Qwen2.5-Coder-1.5B

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Uses

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Direct Use

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

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Results

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

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Framework versions

• PEFT 0.13.0