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

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.


  • Text prompts: The model takes in arbitrary text prompts as input, which can range from short phrases to long passages.


  • 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.


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.

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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StableLM-Base-Alpha is a suite of 3B and 7B parameter decoder-only language models pre-trained on a diverse collection of English datasets. These models are designed to push beyond the context window limitations of existing open-source language models. The 3B model and 7B model are part of this suite. The models are based on the NeoX transformer architecture and developed by Stability AI. They are licensed under the Creative Commons license (CC BY-SA-4.0), allowing for both commercial and non-commercial use as long as attribution is provided. Model Inputs and Outputs The StableLM-Base-Alpha models take in text prompts and generate continuation text. The input prompts can be of any length up to 4096 tokens. The models will then generate new tokens, with the ability to continue the text for up to 64 additional tokens. Inputs Text prompts of up to 4096 tokens Outputs Continued text, with the ability to generate up to 64 additional tokens Capabilities The StableLM-Base-Alpha models excel at a variety of text generation tasks, such as creative writing, summarization, and language modeling. They can be used to generate coherent and contextually relevant text, while maintaining a high level of fluency. What Can I Use It For? The StableLM-Base-Alpha models can be used as a foundation for a wide range of applications, such as: Content generation for blogs, articles, or stories Assistive writing tools to help users generate text Language modeling for downstream tasks like sentiment analysis or text classification Chatbots and conversational agents Summarization of long-form text Things to Try One interesting aspect of the StableLM-Base-Alpha models is their ability to maintain coherence and context over long sequences of text. You can try providing the models with prompts that require extended context, such as multi-paragraph narratives or complex instructions, and see how they respond. Additionally, you can experiment with different decoding strategies, such as adjusting the temperature or top-p sampling, to generate more diverse or controlled outputs.

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StableLM-Tuned-Alpha is a suite of decoder-only language models developed by Stability AI that have been further fine-tuned on various chat and instruction-following datasets beyond the base StableLM-Base-Alpha models. These models range from 3 billion to 7 billion parameters and are designed to be helpful and harmless conversational assistants. The similar models StableLM-Tuned-Alpha-7B and StableCode-Instruct-Alpha-3B build on this base, with the former focused on open-ended chat and the latter specialized for code generation tasks. The Japanese-StableLM-Base-Alpha-7B model is a Japanese-focused variant, demonstrating Stability AI's work in multilingual language modeling. Model Inputs and Outputs StableLM-Tuned-Alpha models are designed to engage in open-ended dialogue. They accept conversational prompts in the format ...... and generate coherent, on-topic responses. Inputs Conversational Prompt**: A prompt containing a system message, user input, and a request for the model to respond. Outputs Text Response**: The model's generated response to continue the conversation. Capabilities StableLM-Tuned-Alpha models are capable of engaging in helpful and engaging conversations on a wide range of topics. They can provide informative answers, generate creative content like stories and jokes, and refuse to participate in anything harmful or unethical. The models demonstrate strong language understanding and generation abilities while maintaining a focus on safety and beneficial interaction. What Can I Use It For? These models are well-suited for building chat-based applications, virtual assistants, and other AI-powered conversational experiences. Their combination of broad knowledge, language skills, and safety considerations make them a powerful foundation for developing helpful and trustworthy AI assistants. For commercial use of these models, users should refer to to obtain the necessary licenses. Things to Try Try providing the models with open-ended prompts that allow them to showcase their versatility, such as asking for creative writing, opinion sharing, or task-oriented assistance. Experiment with different temperature and sampling settings to see how they affect the models' responses. You can also try combining StableLM-Tuned-Alpha with other Stability AI models, like StableCode-Instruct-Alpha-3B, to create more specialized AI assistants.

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stablelm-tuned-alpha-7b is a suite of 3B and 7B parameter decoder-only language models built on top of the StableLM-Base-Alpha models and further fine-tuned on various chat and instruction-following datasets. Developed by Stability AI, stablelm-tuned-alpha-7b is similar to other large language models like stable-code-3b, Starling-LM-7B-alpha, and StableBeluga2 in terms of scale and capabilities. Model inputs and outputs stablelm-tuned-alpha-7b is a text-to-text model, meaning it takes in textual prompts and generates additional text in response. The model uses a special conversational format with system, user, and assistant tokens to structure the interaction. Inputs Textual prompts**: Prompts can be in the form of natural language queries, instructions, or open-ended tasks. Conversation format**: Prompts should be formatted with `, , and ` tokens to indicate the different roles in the conversation. Outputs Generated text**: The model will produce relevant, contextual text in response to the input prompt. The output can include a wide range of content such as answers, stories, code, and more. Capabilities stablelm-tuned-alpha-7b has been fine-tuned on diverse datasets to enable it to engage in helpful and harmless conversations. It can assist with a variety of tasks, from answering questions and providing explanations to generating creative content like poetry and short stories. Importantly, the model is designed to refuse requests that could be considered harmful to users. What can I use it for? stablelm-tuned-alpha-7b can be a useful tool for applications that require natural language understanding and generation, such as chatbots, virtual assistants, and content creation tools. The model's large scale, broad knowledge, and safety-oriented design make it well-suited for use cases that prioritize helpfulness and alignment with user interests. However, as with any large language model, care should be taken to evaluate and fine-tune the model for specific use cases before deployment. Things to try One interesting aspect of stablelm-tuned-alpha-7b is its ability to engage in open-ended conversations and generate creative content. Try providing the model with prompts that encourage it to write poetry, short stories, or even jokes - the results can be quite entertaining and thought-provoking. Additionally, you can explore the model's safety features by asking it to perform tasks that could be considered harmful, and observe how it responds.

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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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