Qwen2.5-Coder-0.5B-Instruct-AWQ

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

CommonProductProgrammingCode GenerationCode Reasoning

Qwen2.5-Coder represents the latest series of the Qwen large language models, focusing on code generation, reasoning, and repair. Built on the robust foundations of Qwen2.5, with a training corpus expanded to 5.5 trillion tokens that includes source code, textual code bases, and synthetic data, Qwen2.5-Coder-32B has emerged as the leading open-source code LLM, matching the coding capabilities of GPT-4o. This model is a 4-bit instruction-tuned version of the 0.5B parameters, featuring characteristics such as causal language modeling, pre-training and fine-tuning, as well as a transformer architecture.

Code Generation: Significantly enhances the ability to generate code
catering to the needs of various developers.
Code Reasoning: Enhances the model's understanding of code logic.
Code Repair: Improves the ability to detect and fix code errors.
Comprehensive Programming Foundation: Not only strengthens coding capabilities but also maintains advantages in mathematics and general skills.
Causal Language Model: Suitable for generating coherent sequences of code.
AWQ 4-bit Quantization: Optimizes model size and inference speed.
Long Context Support: Supports context lengths of up to 32
768 tokens.

The target audience includes developers and programmers
especially those who require efficient tools for code generation
comprehension
and debugging. The Qwen2.5-Coder series models enhance programming efficiency and quality by providing robust capabilities in code generation and understanding.

Developers use Qwen2.5-Coder to generate code for sorting algorithms.
Software engineers utilize the model to fix bugs in existing code.
In programming education
teachers use the model to assist students in understanding code logic.

1. Visit the Hugging Face platform and search for the Qwen2.5-Coder-0.5B-Instruct-AWQ model.
2. Import AutoModelForCausalLM and AutoTokenizer based on the code snippets provided on the page.
3. Load the model and tokenizer using the model name.
4. Prepare input prompts
such as writing a quicksort algorithm.

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Monthly Visits

21315886

Bounce Rate

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5.2

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Qwen2.5-Coder-14B-Instruct — A 14 billion parameter code generation model in the Qwen2.5-Coder series.

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Qwen2.5-Coder-1.5B-Instruct-GGUF — The Qwen2.5-Coder series features a 1.5B parameter instruction-tuned model.

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Qwen2.5-Coder-32B-Instruct-GPTQ-Int4 — An open-source language model for code generation with 3.25 billion parameters.

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