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Qwen2.5 Coder 0.5B Instruct GPTQ Int8
Qwen2.5-Coder-0.5B-Instruct-GPTQ-Int8
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Qwen2.5 Coder 0.5B Instruct GPTQ Int8
Coding AssistantDevelopment & Tools#Code Generation#Code Reasoning#Code Debugging#Code Agent#GPTQ Quantization#TransformersStandard PicksOpen Source
Overview :
Qwen2.5-Coder is the latest series of the Qwen large language model, focusing on code generation, reasoning, and debugging. Based on the robust Qwen2.5, this series significantly improves code generation, reasoning, and repair capabilities by incorporating 55 trillion training tokens, including source code, text code grounding, and synthetic data. The Qwen2.5-Coder-32B has emerged as the most advanced open-source large language model for code generation, matching the coding capabilities of GPT-4o. Additionally, Qwen2.5-Coder offers a more comprehensive foundation for real-world applications, such as code agents, enhancing coding abilities while maintaining strengths in mathematics and general proficiency.
Target Users :
The target audience is developers and programmers, especially professionals who need to generate, reason about, and debug code during the software development process. Qwen2.5-Coder enhances their development efficiency and code quality by providing powerful code generation and understanding capabilities.
Total Visits： 29.7M
Top Region： US(17.94%)
Website Views ： 45.5K
Use Cases
Developers use Qwen2.5-Coder to generate code for a quicksort algorithm.
During code debugging, Qwen2.5-Coder helps developers locate and fix potential code errors.
Acting as a code agent, Qwen2.5-Coder automates some repetitive coding tasks, enhancing development efficiency.
Features
Code Generation: Significantly enhances code generation capabilities to meet the diverse needs of developers.
Code Reasoning: Improves the model's understanding of code logic, boosting code reasoning skills.
Code Debugging: Assists developers in identifying and fixing errors in their code.
Code Agent: Provides a more comprehensive foundation for real-world applications, enhancing coding capabilities.
Mathematics and General Skills: Maintains strengths in mathematics and general abilities.
GPTQ 8-bit Quantization: Optimizes model performance, reduces model size, and increases inference speed.
Full 32,768 Token Context Length: Supports processing of longer code segments.
Transformers-Based Architecture: Utilizes advanced Transformers architecture to improve model performance.
How to Use
1. Visit the Hugging Face website and search for the Qwen2.5-Coder-0.5B-Instruct-GPTQ-Int8 model.
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. Process the input prompt using the tokenizer.apply_chat_template method.
6. Feed the processed text into the model to generate code.
7. Use the tokenizer.batch_decode method to convert the generated code IDs back into text.
8. Retrieve and review the generated code to ensure it meets the requirements.
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