Qwen2.5-Coder-0.5B-Instruct-GPTQ-Int4

0.5B parameter code generation model from the Qwen2.5-Coder series

CommonProductProgrammingCode GenerationCode Inference

Qwen2.5-Coder is the latest series in the Qwen large language model lineup, focusing on code generation, inference, and debugging. Built upon the powerful Qwen2.5 framework, this model was trained on 550 trillion sources, including source code, text code foundations, and synthetic data, making it the most advanced open-source code language model available today. It not only matches the programming capabilities of GPT-4o but also maintains an edge in mathematics and general competencies. The Qwen2.5-Coder-0.5B-Instruct-GPTQ-Int4 model features a 4-bit quantized instruction-tuned architecture, incorporating causal language modeling, pre-training and fine-tuning, as well as a transformers architecture.

Code Generation: Significantly enhances code generation capabilities across multiple programming languages.
Code Inference: Improves the model's understanding of code logic
increasing inference accuracy.
Code Debugging: Aids developers in identifying and fixing errors in code.
Comprehensive Parameter Coverage: The model comprises 0.49B parameters
with 0.36B non-embedding parameters
covering a wide range of programming scenarios.
Multi-layer Architecture: Features a 24-layer network structure
providing deep code understanding and generation capabilities.
Long Text Support: Handles context lengths of up to 32

\The target audience includes developers and programming enthusiasts
especially professionals seeking efficient code generation
inference
and debugging tools. The Qwen2.5-Coder-0.5B-Instruct-GPTQ-Int4 model
renowned for its robust code handling capabilities and highly efficient quantization technology

Developers use the model to generate code for specific algorithms
such as quicksort.
During code review
leverage the model for inferring code logic to enhance code quality.
When faced with programming challenges

1. Install and import the necessary libraries
such as transformers and torch.
2. Load the model and tokenizer from Hugging Face: `model = AutoModelForCausalLM.from_pretrained(model_name)` and `tokenizer = AutoTokenizer.from_pretrained(model_name)`.
3. Prepare your input prompt
such as the requirements for an algorithm.

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