CodeQwen1.5-7B-Chat
Maintainer: Qwen
189
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
CodeQwen1.5-7B-Chat is a transformer-based language model developed by Qwen. It is a code-specific version of the larger Qwen1.5 model series, which includes language models of various sizes. CodeQwen1.5-7B-Chat is trained on a large amount of code data and excels at tasks like text-to-SQL, bug fixing, and more. Compared to the original Qwen1.5 model, CodeQwen1.5-7B-Chat has strong code generation capabilities and can handle long contexts of up to 64K tokens across 92 coding languages.
Model inputs and outputs
Inputs
- Text:
CodeQwen1.5-7B-Chatcan accept text inputs for various code-related tasks, such as prompts for code generation, text-to-SQL, and bug fixes.
Outputs
- Text: The model generates text outputs, which can include code, SQL queries, or natural language responses related to the input.
Capabilities
CodeQwen1.5-7B-Chat demonstrates impressive performance across a range of benchmarks, including text-to-SQL, bug fixing, and more. It can generate high-quality code and maintain coherence over long contexts of up to 64K tokens.
What can I use it for?
CodeQwen1.5-7B-Chat can be a valuable tool for developers and data analysts who need assistance with code-related tasks. It can be used to generate code snippets, fix bugs, translate natural language to SQL queries, and more. The model's strong performance and ability to handle long contexts make it well-suited for complex, multi-step coding and data analysis projects.
Things to try
One interesting aspect of CodeQwen1.5-7B-Chat is its support for a wide range of coding languages, which allows users to directly enhance the model's capabilities in specific languages without the need to expand the vocabulary. This can be particularly useful for developers working in less common programming languages or those who need multilingual support for their projects.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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