Qwen2.5-Coder-1.5B-Instruct-GPTQ-Int8

1.5B parameter code generation model from the Qwen2.5-Coder series

CommonProductProgrammingCode GenerationCode Reasoning

Qwen2.5-Coder is the latest series of the Qwen large language models, focusing on code generation, reasoning, and debugging. Based on the powerful Qwen2.5 architecture, this model was trained on 550 trillion source codes, text-code associations, synthetic data, and more, making it a leader among current open-source code language models. It not only enhances programming capabilities but also retains advantages in mathematics and general-purpose tasks.

Code Generation: Significantly improves code generation capabilities
assisting developers in quickly accomplishing programming tasks.
Code Reasoning: Enhances the model's understanding of code logic
improving the accuracy of code analysis.
Code Debugging: Automatically detects and rectifies errors in code
enhancing code quality.
Full Parameter Scale Coverage: Provides different model sizes ranging from 50 million to 3.2 billion parameters
catering to various developer needs.
Real-world Application Support: Offers comprehensive capabilities for practical applications like code assistance.
8-bit Quantization: Utilizes GPTQ 8-bit quantization technology to optimize model performance and resource consumption.

\Target audience includes developers and programming enthusiasts
especially those who need to quickly generate
understand
and debug code. This product enhances their development efficiency and code quality by providing powerful code generation and comprehension capabilities.\

A developer uses Qwen2.5-Coder to generate code for a quicksort algorithm.
A software engineer utilizes the model to fix bugs in existing code.
Programming educators use this model to assist students in understanding complex programming concepts.

1. Access the Hugging Face platform and locate the Qwen2.5-Coder-1.5B-Instruct-GPTQ-Int8 model.
2. Import the necessary libraries and modules as per the code examples provided on the page.
3. Load the model and tokenizer using AutoModelForCausalLM and AutoTokenizer.from_pretrained methods.
4. Prepare input prompts
such as a request to write code for a specific function.

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