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Qwen2.5 Coder 1.5B Instruct AWQ
Overview :
Qwen2.5-Coder is the latest series of the Qwen large language model, designed for code generation, reasoning, and fixing. Built on the powerful Qwen2.5, this model has been trained on 550 trillion source codes, text code foundations, and synthetic data, elevating its coding capabilities to the forefront of open-source code LLMs. It not only enhances coding abilities but also maintains strengths in mathematics and general capabilities.
Target Users :
The target audience includes developers and programmers, especially those who need to quickly generate, understand, and fix code in their projects. The Qwen2.5-Coder series models provide robust code assistance to help developers enhance their productivity and code quality.
Use Cases
A developer utilizes Qwen2.5-Coder to generate code for a quicksort algorithm.
During code review, Qwen2.5-Coder is used to identify potential coding errors and propose improvements.
While developing new features, developers leverage Qwen2.5-Coder to rapidly create prototype code, accelerating the development process.
Features
Code Generation: Significantly boosts the ability to generate high-quality code based on instructions.
Code Reasoning: Enhances understanding of code logic and structure, aiding in code analysis and optimization.
Code Fixing: Can identify errors in the code and provide fixing suggestions to improve code quality.
Full Parameter Coverage: Covers six mainstream model sizes to meet the needs of different developers.
Comprehensive Application Basis: Suitable for real-world applications like code assistants to enhance coding capabilities.
High Performance: Achieves quantization of the model while maintaining high performance, reducing computational requirements.
Long Context Support: Supports context lengths of up to 32,768 tokens, making it suitable for handling long code snippets.
How to Use
1. Visit the Hugging Face website and search for the Qwen2.5-Coder-1.5B-Instruct-AWQ model.
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2. Import the necessary libraries and modules as per the code examples provided on the page.
3. Use AutoModelForCausalLM and AutoTokenizer to load the model and tokenizer from the pre-trained model.
4. Prepare your input prompt, such as writing an algorithm or resolving a coding problem.
5. Process the input prompt using the tokenizer to generate model input.
6. Use the model.generate method to produce code.
7. Convert the generated code IDs into text format to obtain the final code output.
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