Qwen2.5-Coder-3B-Instruct-GPTQ-Int4

3B parameter instruction-tuned model within the Qwen2.5-Coder series

CommonProductProgrammingCode GenerationCode Inference

Qwen2.5-Coder is the latest series in the Qwen large language model, specifically designed for code generation, inference, and debugging. The model is based on Qwen2.5, extending the training tokens to 5.5 trillion, incorporating source code, textual code foundations, synthetic data, etc. Qwen2.5-Coder-32B stands out as a top performer among open-source code LLMs, matching the encoding capabilities of GPT-4o. This model is a GPTQ-quantized 4-bit instruction-tuned 3B parameter Qwen2.5-Coder model, featuring causal language modeling, pre-training and post-training phases, and a transformers architecture.

Code Generation: Significantly enhances code generation capabilities
helping developers achieve programming tasks quickly.
Code Inference: Improves the model's understanding of code logic
enhancing code analysis and inference abilities.
Code Debugging: Assists developers in diagnosing and fixing errors in code
improving code quality.
Comprehensive Parameter Coverage: Offers models with parameter sizes ranging from 0.5 to 3.2 billion
catering to different developer needs.
Real-World Application Foundation: Strengthens the model's performance in mathematical and general capabilities
laying a solid foundation for practical applications.

\The target audience includes software developers
programming enthusiasts
and researchers needing code generation
inference
and debugging. The Qwen2.5-Coder series models significantly enhance programming efficiency

Developers using Qwen2.5-Coder to generate code for a sorting algorithm.
Researchers leveraging the model for logical reasoning and analysis of specific code snippets.
Software engineers employing the model to fix bugs in existing code
enhancing code stability.

1. Visit the Hugging Face platform and search for the Qwen2.5-Coder-3B-Instruct-GPTQ-Int4 model.
2. Import necessary libraries as per the code examples provided on the page
such as transformers and torch.
3. Load the model and tokenizer using AutoModelForCausalLM and AutoTokenizer.from_pretrained methods.
4. Prepare the input prompt

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