Maintainer: stabilityai

Total Score


Last updated 5/28/2024


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

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Model overview

StableCode-Completion-Alpha-3B-4K is a 3 billion parameter decoder-only code completion model pre-trained on a diverse set of programming languages that topped the 2023 StackOverflow Developer Survey. It was developed by Stability AI, a leading AI research company. The model is based on the GPT-NeoX library and utilizes techniques like Rotary Position Embeddings and LayerNorm bias terms.

Similar models include the StableCode-Completion-Alpha-3B, which is a 3 billion parameter model trained on a similar dataset but with a longer context length of 16,384 tokens. The StableCode-Instruct-Alpha-3B is an instruction-tuned version of the base completion model, and the stable-code-3b is a larger 3 billion parameter model trained on an even broader set of code and text data.

Model inputs and outputs


  • Code context: The model takes in a code context of up to 4,096 tokens and generates new code completions.


  • Code completions: The model generates new code completions based on the provided context, with a maximum of 48 new tokens.


StableCode-Completion-Alpha-3B-4K demonstrates strong performance on code completion tasks across a variety of programming languages, including Python, C++, JavaScript, Java, and PHP. The model can generate coherent and relevant code continuations based on the provided context, making it a useful tool for developers looking to boost their productivity.

What can I use it for?

The StableCode-Completion-Alpha-3B-4K model can be leveraged in a variety of applications, such as:

  • Code editors and IDEs: Integrating the model into code editing tools to provide intelligent code completion suggestions, saving developers time and effort.
  • Prototyping and experimentation: Exploring new ideas and quickly generating initial code implementations by relying on the model's generative capabilities.
  • Educational resources: Developing interactive coding tutorials or exercises that utilize the model to help learners understand programming concepts.

Things to try

One interesting aspect of StableCode-Completion-Alpha-3B-4K is its ability to generate code based on a long context window of up to 4,096 tokens. This can be particularly useful for tasks like refactoring or extending existing code bases, where the model can leverage the broader context to generate coherent and relevant completions.

Another interesting capability to explore is the model's performance on specific programming languages or code domains. By testing the model on a range of tasks and benchmarks, developers can gain insights into the model's strengths and limitations, and identify areas for further fine-tuning or customization.

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-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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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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StableLM-Base-Alpha is a suite of 3B and 7B parameter decoder-only language models pre-trained on a diverse collection of English and Code datasets. It is designed to push beyond the context window limitations of existing open-source language models. The model was developed by Stability AI. Similar models include StableLM-Tuned-Alpha, which are fine-tuned versions of the base model built for chat and instruction-following tasks, and StableCode-Completion-Alpha-3B and StableCode-Instruct-Alpha-3B, which are specialized for code completion and instruction-following code generation tasks. Model inputs and outputs The StableLM-Base-Alpha models are designed to take in text inputs and generate continuations or completions. The models have a large context window of up to 4096 tokens, allowing them to leverage long-range dependencies in the input text. Inputs Text prompts**: The model takes in arbitrary text prompts as input, which can range from short phrases to long passages. Outputs Generated text**: The model outputs generated text that continues or completes the input prompt. The length of the generated output can be controlled via parameters like max_new_tokens. Capabilities The StableLM-Base-Alpha models excel at general text generation tasks, such as writing, summarization, and open-ended question answering. The large context window and powerful language modeling capabilities allow the models to produce coherent and contextually-relevant text. What can I use it for? The StableLM-Base-Alpha models can be used for a variety of applications, such as: Content generation**: Generating long-form articles, stories, and other types of written content. Summarization**: Summarizing long passages of text into concise summaries. Question answering**: Answering open-ended questions based on provided context. Conversational AI**: Building chatbots and virtual assistants that can engage in natural conversations. When using the model, it's important to be mindful of potential biases and limitations, and to avoid treating the model outputs as authoritative sources of information. Things to try One interesting thing to try with the StableLM-Base-Alpha models is using the large context window to generate coherent and cohesive long-form text. Prompt the model with an engaging opening paragraph and see how it continues the story or expands on the initial idea. You can also experiment with different temperature and sampling settings to adjust the creativity and diversity of the generated text. Another interesting use case is to leverage the model's strong language understanding capabilities for tasks like question answering or summarization. Provide the model with detailed context and see how it can extract key information and generate concise, relevant responses. Overall, the StableLM-Base-Alpha models are a powerful and versatile tool for a wide range of natural language processing tasks. By exploring their capabilities and limitations, you can gain valuable insights into the current state of large language models and how they can be applied to real-world problems.

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