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Maintainer: stabilityai

Total Score


Last updated 5/17/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model Overview

stable-code-instruct-3b is a 2.7 billion parameter decoder-only language model tuned from the [object Object] model. This model was trained on a mix of publicly available datasets and synthetic datasets using Direct Preference Optimization (DPO). The model demonstrates state-of-the-art performance on the MultiPL-E metrics across multiple programming languages tested using BigCode's Evaluation Harness, and on the code portions of MT Bench.

This instruct-tuned model is optimized for general purpose code and software engineering tasks, as well as SQL-related generation and conversation. It outperforms similar-sized models on a range of programming-focused benchmarks.

Model Inputs and Outputs


  • Text prompts for code generation, including instructions, software requirements, or other context


  • Generated code snippets or complete programs in a variety of programming languages
  • Responses to prompts related to software engineering tasks, such as answering questions or providing explanations


stable-code-instruct-3b is capable of generating high-quality code in multiple programming languages, including Python, C++, JavaScript, Java, and PHP. It can assist with a wide range of software engineering tasks, such as writing functions, implementing algorithms, and solving coding challenges. The model also demonstrates strong conversational abilities, allowing users to engage in back-and-forth dialogues about code-related topics.

What Can I Use It For?

You can use stable-code-instruct-3b to aid in your software development workflows. Some potential use cases include:

  • Generating starter code for new projects or features
  • Assisting with debugging and troubleshooting by explaining code or suggesting fixes
  • Automating repetitive coding tasks, such as boilerplate generation
  • Enhancing productivity by allowing you to explore and validate ideas through interactive prompts

When using the model commercially, please refer to for licensing information.

Things to Try

One interesting capability of stable-code-instruct-3b is its ability to handle "Fill in the Middle" (FIM) prompts, where the model is tasked with generating the middle portion of a code snippet while the beginning and end are provided. This can be a useful feature when exploring different approaches to a problem or when trying to understand how a specific algorithm or data structure might be implemented.

Another interesting aspect of the model is its strong performance on SQL-related tasks. You can try prompting the model with database schema information or SQL queries and see how it responds, potentially generating new queries or suggesting optimizations.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models




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StableCode-Instruct-Alpha-3B is a 3 billion parameter decoder-only instruction tuned code model pre-trained on a diverse set of programming languages that topped the StackOverflow developer survey. It builds upon the StableCode-Completion-Alpha-3B model, with additional fine-tuning on code instruction datasets. This model demonstrates strong performance across a range of programming languages, outperforming some larger models like CodeLLama and Wizard Coder on the MultiPL-E benchmark. Model inputs and outputs Inputs Text instructions for generating code Outputs Generated code based on the provided instructions Capabilities StableCode-Instruct-Alpha-3B is capable of generating code based on natural language instructions. It can handle a wide variety of programming languages and tasks, from simple utility functions to more complex algorithms. The model's strong performance on the MultiPL-E benchmark suggests it is a capable code generation tool across many domains. What can I use it for? StableCode-Instruct-Alpha-3B can be used as a foundation for building applications that require code generation from natural language, such as programming assistants, code editors with intelligent autocomplete, and even low-code/no-code platforms. Developers can fine-tune the model further on their own datasets and use cases to create custom code generation tools tailored to their needs. Things to try One interesting aspect of StableCode-Instruct-Alpha-3B is its ability to generate code in multiple programming languages. Developers can experiment with providing instructions in natural language and observe how the model generates code in different languages, potentially discovering new ways to leverage this cross-language capability. Additionally, exploring the model's performance on more complex programming tasks, such as implementing algorithms or building full applications, can provide valuable insights into its strengths and limitations.

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stable-code-3b is a 2.7B parameter decoder-only language model pre-trained on 1.3 trillion tokens of diverse textual and code datasets. Developed by Stability AI, stable-code-3b demonstrates state-of-the-art performance on the MultiPL-E metrics across multiple programming languages compared to models of similar size. It outperforms other code generation models like CodeLLama, Deepseek Coder, and Wizard Coder on tasks like Python, C++, and JavaScript. Model inputs and outputs stable-code-3b is a text-to-text model, taking in prompts as input and generating relevant code as output. It can handle long context, with the ability to generate code based on sequences up to 16,384 tokens. The model also supports a "Fill in Middle" (FIM) capability, where it can complete partially-written code snippets. Inputs Text prompts for code generation, up to 16,384 tokens Partial code snippets for the "Fill in Middle" capability Outputs Generated code in one of 18 programming languages the model was trained on, including Python, C++, JavaScript, Java, PHP, and Rust Capabilities stable-code-3b excels at generating high-quality, functional code across a variety of programming languages. It can be used to write entire programs from scratch, or fill in missing sections of existing code. The model's strong performance on the MultiPL-E benchmark suggests it can handle a wide range of coding tasks and produce code that is syntactically correct and logically sound. What can I use it for? stable-code-3b can be a valuable tool for developers, data scientists, and anyone working with code. It could be used to speed up prototyping and development by automatically generating boilerplate code or completing repetitive tasks. The model could also be fine-tuned on domain-specific datasets to create customized code generation models for specialized applications. Things to try Experiment with different prompting techniques to see how stable-code-3b responds. Try providing high-level descriptions of the functionality you want, or giving it partially-completed code snippets to fill in. You can also try adjusting parameters like temperature and top-k/top-p values during generation to control the creativity and diversity of the output. By exploring the model's capabilities, you can unlock new ways to streamline your coding workflows.

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StableCode-Completion-Alpha-3B is a 3 billion parameter decoder-only code completion model developed by Stability AI. It was pre-trained on a diverse set of programming languages that were the top used languages based on the 2023 Stack Overflow developer survey. This model can be compared to the StableCode-Instruct-Alpha-3B model, which is the instruction-tuned version, and the Stable Code 3B model, which is a larger 3 billion parameter decoder-only language model pre-trained on code and text. Model Inputs and Outputs StableCode-Completion-Alpha-3B is a code generation model designed to provide single or multi-line code completions from a long context window of up to 16,000 tokens. The model takes in code context as input and generates relevant code completions as output. Inputs Code context of up to 16,000 tokens Outputs Single or multi-line code completions relevant to the provided context Capabilities StableCode-Completion-Alpha-3B demonstrates strong performance on code generation tasks, outperforming other similarly sized models on benchmarks like MultiPL-E across multiple programming languages. The model can be used to assist developers by providing intelligent code suggestions and completions based on the context. What Can I Use It For? StableCode-Completion-Alpha-3B can be integrated into a variety of developer tools and applications to enhance the coding experience. For example, it could be used to power intelligent code editors that provide real-time code completions, or integrated into chatbots and virtual assistants to help developers with coding tasks. The model's broad language support also makes it useful for cross-language development and collaboration. Things to Try One interesting aspect of StableCode-Completion-Alpha-3B is its ability to generate code from a long context window. This allows the model to understand and continue complex coding patterns, which could be useful for tasks like implementing algorithms, refactoring code, or expanding on existing functionality. Developers could experiment with providing the model with partially completed code snippets or pseudocode to see how it continues the logic.

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StableLM-3B-4E1T is a 3 billion parameter decoder-only language model developed by Stability AI. The model was pre-trained on 1 trillion tokens of diverse English and code datasets for 4 epochs. Similar models in the Stable LM collection include the Stable LM 2 12B and Stable LM 2 1.6B, which are 12.1 and 1.6 billion parameter models respectively, pre-trained on 2 trillion tokens. Model inputs and outputs StableLM-3B-4E1T is a text generation model that can be used to generate coherent and contextual text based on a given prompt. The model takes natural language text as input and outputs a continuation of the text. Inputs Natural language text prompts Outputs Continued text generated by the model, based on the input prompt Capabilities StableLM-3B-4E1T demonstrates strong performance on a variety of natural language processing tasks, including text generation, summarization, and question answering. The model is particularly adept at producing coherent and contextual text, making it well-suited for applications such as content creation, dialogue systems, and language-based AI assistants. What can I use it for? StableLM-3B-4E1T can be used as a foundational model for a wide range of natural language processing applications. For example, it could be fine-tuned for tasks like creative writing, code generation, or even chatbots and virtual assistants. The model's large scale and diverse pre-training dataset make it a powerful starting point for many language-based AI projects. Things to try One interesting aspect of StableLM-3B-4E1T is its ability to handle long-form text generation. By leveraging the 4,096 token sequence length, the model can produce coherent and contextual text that maintains a consistent narrative over an extended period. This capability could be particularly useful for applications like story generation, report writing, or even novel composition.

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