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Qwen2.5 Coder 0.5B Instruct AWQ
Overview :
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.
Target Users :
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.
Use Cases
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.
Features
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.
How to Use
1. Visit the Hugging Face platform and search for the Qwen2.5-Coder-0.5B-Instruct-AWQ model.
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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.
5. Process the input message using the tokenizer's apply_chat_template method.
6. Convert the processed text into the model's input format.
7. Generate code using the model.generate method.
8. Convert the generated code IDs back into text format to obtain the final code output.
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