💻 programming

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

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

#code generation
#code fix
#code reasoning
#long context
#GPTQ quantification
Qwen2.5-Coder-1.5B-Instruct-GPTQ-Int8

Product Details

Qwen2.5-Coder is the latest series of Qwen large-scale language models, focusing on code generation, code reasoning and code repair. Based on the powerful Qwen2.5, this model uses 5.5 trillion source codes, text code associations, synthetic data, etc. in training, making it the leader among current open source code language models. This model not only enhances programming capabilities, but also maintains its advantages in mathematics and general capabilities.

Main Features

1
Code generation: Significantly improve code generation capabilities to help developers quickly complete programming tasks.
2
Code reasoning: Enhance the model’s understanding of code logic and improve the accuracy of code analysis.
3
Code repair: Automatically detect and repair errors in the code to improve code quality.
4
Full parameter scale coverage: Provides different model scales from 0.5 billion to 3.2 billion parameters to meet the needs of different developers.
5
Practical application foundation: Provide more comprehensive capability support for practical applications such as code agents.
6
8-bit quantization: Use GPTQ 8-bit quantization technology to optimize model performance and resource consumption.
7
Long context support: supports context lengths up to 32768 tokens, suitable for processing complex code.

How to Use

1
1. Visit the Hugging Face platform and find the Qwen2.5-Coder-1.5B-Instruct-GPTQ-Int8 model.
2
2. Import the necessary libraries and modules according to the code examples provided on the page.
3
3. Load the model and tokenizer and use the AutoModelForCausalLM and AutoTokenizer.from_pretrained methods.
4
4. Prepare input prompts, such as writing a code request for a specific feature.
5
5. Use the model to generate code, pass the model.generate method and set the max_new_tokens parameter.
6
6. Get the generated code ID and use the tokenizer.batch_decode method to convert the ID into readable code text.
7
7. Analyze the generated code and adjust it as needed or use it directly.

Target Users

The target audience is developers and programming enthusiasts, especially professionals who need to quickly generate, understand and fix code. This product helps them improve development efficiency and code quality by providing powerful code generation and understanding capabilities.

Examples

The developer uses Qwen2.5-Coder to generate the code for a quick sort algorithm.

Software engineers use models to fix bugs in existing code.

Programming educators use this model to assist students in understanding complex programming concepts.

Quick Access

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