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Qwen2.5 Coder 1.5B Instruct GPTQ Int4
Overview :
Qwen2.5-Coder is the latest series from the Qwen large language model, focusing on code generation, reasoning, and debugging. Built upon the powerful Qwen2.5 framework, this model has been trained on 550 trillion source code, text-code correlations, and synthesized data, making it one of the leading open-source code language models today, rivaling GPT-4 in coding ability. Additionally, Qwen2.5-Coder offers comprehensive real-world application capabilities, such as code agents, enhancing coding proficiency while maintaining strengths in mathematical and general skills.
Target Users :
Target audience includes developers, programming enthusiasts, and professionals in need of code assistance. The Qwen2.5-Coder series models are particularly well-suited for developers who require rapid generation, understanding, and debugging of code. They are also applicable in the field of programming education for teaching and learning purposes.
Use Cases
A developer uses Qwen2.5-Coder to generate code for a quicksort algorithm.
An educator utilizes the model to explain code logic, aiding students in understanding algorithms.
Companies use the model to automate code reviews, improving code quality and development efficiency.
Features
Code Generation: Significantly enhances coding capabilities, able to generate high-quality code based on requirements.
Code Reasoning: Improves code reasoning capabilities to help understand code logic and structure.
Code Debugging: Increases code debugging capabilities, able to identify and fix errors in code.
Comprehensive Language Support: Covers multiple mainstream programming languages to meet diverse developer needs.
High Performance: With 1.54 billion parameters and a 28-layer network structure, it offers robust performance.
4-bit Quantization: Utilizes GPTQ's 4-bit quantization technology to optimize model size and inference speed.
Long Text Support: Supports context lengths of up to 32,768 tokens.
Open Source: The model is open-sourced under the Apache-2.0 license, facilitating community use and contributions.
How to Use
1. Install the Hugging Face transformers library, ensuring the version meets the requirements.
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2. Use AutoModelForCausalLM and AutoTokenizer to load the model and tokenizer.
3. Prepare input prompts, such as requests for writing specific function code.
4. Utilize the model to generate code, adjusting parameters to control the length and quality of the output.
5. Analyze the generated code and make adjustments and optimizations as needed.
6. Integrate the generated code into projects or use it for teaching and learning purposes.
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