Qwen2.5-Coder-0.5B-Instruct-GPTQ-Int8

A 0.5B parameter instruction-tuned model within the Qwen2.5-Coder series.

CommonProductProgrammingCode GenerationCode Reasoning

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.

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.

\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.\

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

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.

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