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Qwen2.5 Coder 14B Instruct AWQ
Overview :
Qwen2.5-Coder is a series of large language models specifically designed for coding, ranging from 0.5 billion to 3.2 billion parameters to meet various developer needs. The model shows significant improvements in code generation, reasoning, and repair, leveraging a powerful Qwen2.5 framework trained on 5.5 trillion tokens, including source code, textual code bases, and synthetic data. Qwen2.5-Coder-32B is currently the most advanced open-source large language model for code generation, matching the coding capabilities of GPT-4o. Additionally, the model supports long contexts of up to 128K tokens and employs AWQ 4-bit quantization technology to enhance its efficiency and performance.
Target Users :
The target audience includes developers and programmers, especially those who need to handle large amounts of code and complex projects. Qwen2.5-Coder-14B-Instruct-AWQ enhances their development efficiency, reduces errors, and optimizes code quality by providing robust capabilities for code generation, reasoning, and repair.
Use Cases
Developers use Qwen2.5-Coder to generate code for sorting algorithms.
When managing large software projects, the long context support is utilized to maintain code coherence.
During code reviews, the code repair feature is used to identify and fix potential bugs.
Features
Code generation: Significantly improves code generation capabilities, helping developers quickly implement code logic.
Code reasoning: Enhances the model's understanding of code logic, improving the accuracy of code analysis.
Code repair: Assists developers in identifying and fixing errors in the code.
Long context support: Supports long contexts of up to 128K tokens, suitable for handling large codebases.
AWQ 4-bit quantization: Increases operational efficiency and reduces resource consumption.
Pre-training and fine-tuning: The model undergoes pre-training and fine-tuning to optimize performance.
Diverse encoding techniques: Utilizes a transformers architecture that includes RoPE, SwiGLU, RMSNorm, and Attention QKV biases.
How to Use
1. Visit the Hugging Face website and search for the Qwen2.5-Coder-14B-Instruct-AWQ model.
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2. Import AutoModelForCausalLM and AutoTokenizer using the code snippets provided on the page.
3. Load the model and tokenizer using the model name.
4. Prepare input prompts, such as writing a quicksort algorithm.
5. Use the tokenizer.apply_chat_template method to process the input message.
6. Convert the processed text into model input.
7. Generate code using the model.generate method.
8. Convert the generated code IDs back into text format to obtain the final code output.
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