💻 programming

Qwen2.5-Coder-1.5B-Instruct-GPTQ-Int4

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

#code generation
#Programming assistance
#Open source model
#code fix
#code reasoning
Qwen2.5-Coder-1.5B-Instruct-GPTQ-Int4

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 includes 5.5 trillion source codes, text code associations, synthetic data, etc. in training. It is currently the leader in open source code language models, and its coding capabilities are comparable to GPT-4. In addition, Qwen2.5-Coder also has a more comprehensive real-world application foundation, such as code agents, etc., which not only enhances coding capabilities, but also maintains its advantages in mathematics and general capabilities.

Main Features

1
Code generation: Significantly improve code generation capabilities and be able to generate high-quality code according to needs.
2
Code reasoning: Enhance code reasoning capabilities and help understand code logic and structure.
3
Code repair: Improve code repair capabilities and be able to identify and repair errors in the code.
4
Comprehensive programming language support: Covers a variety of mainstream programming languages ​​to meet the needs of different developers.
5
High performance: 1.54B parameter volume, 28-layer network structure, providing powerful performance.
6
4-bit quantization: Use GPTQ 4-bit quantization technology to optimize model size and inference speed.
7
Long text support: Supports context lengths up to 32,768 tokens.
8
Open source: The model is open source under the Apache-2.0 license, making it easy for the community to use and contribute.

How to Use

1
1. Install Hugging Face’s transformers library and make sure the version meets the requirements.
2
2. Use AutoModelForCausalLM and AutoTokenizer to load the model and tokenizer.
3
3. Prepare input prompts, such as writing a code request for a specific feature.
4
4. Use the model to generate code, and you can control the length and quality of the generated code by adjusting parameters.
5
5. Analyze the generated code and make adjustments and optimizations as needed.
6
6. Integrate the generated code into a project or use it for teaching and learning purposes.

Target Users

The target audience is developers, programming enthusiasts, and professionals who need coding assistance. The Qwen2.5-Coder series model, with its powerful code generation and reasoning capabilities, is particularly suitable for developers who need to quickly generate, understand, and repair code. It is also suitable for the field of programming education that requires code teaching and learning.

Examples

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

Programming educators use models to explain code logic and help students understand algorithms.

Enterprises use models to automate code reviews and improve code quality and development efficiency.

Quick Access

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