stablelm-2-12b
Maintainer: stabilityai
103
🏷️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
Stable LM 2 12B is a 12.1 billion parameter decoder-only language model developed by Stability AI. It was pre-trained on 2 trillion tokens of diverse multilingual and code datasets for two epochs. The model is part of the Stable LM 2 series, which also includes the Stable LM 2 1.6B and Stable Code 3B models. Compared to the smaller 1.6B version, the 12B model has significantly more parameters and demonstrates improved performance on various benchmarks.
Model inputs and outputs
The Stable LM 2 12B model is a text generation model that takes natural language prompts as input and generates coherent, contextual text output. The model can be used for a variety of natural language tasks, such as summarization, translation, and open-ended generation.
Inputs
- Natural language prompts in various languages, with a focus on English
Outputs
- Coherent, context-aware text generated in response to the input prompts
- The model can generate text of varying lengths, from short phrases to multi-paragraph passages
Capabilities
The Stable LM 2 12B model demonstrates strong performance on a range of natural language tasks, including open-ended generation, summarization, and translation. It can be used to generate human-like text on a variety of topics, from creative writing to technical documentation. The model's large size and diverse training data allow it to capture a wide range of linguistic patterns and knowledge.
What can I use it for?
Stable LM 2 12B can be a powerful tool for developers and researchers working on natural language processing applications. Some potential use cases include:
- Content generation: The model can be used to generate original text for applications like creative writing, article generation, and chatbots.
- Summarization: The model can be fine-tuned to summarize longer passages of text, making it useful for tasks like document summarization.
- Translation: The multilingual capabilities of the model can be leveraged for machine translation between supported languages.
- Knowledge-based applications: The model's broad training data can be leveraged to build applications that require access to a wide range of information, such as question-answering systems.
However, as a large language model, Stable LM 2 12B may exhibit biases or generate unsafe content. Users should carefully evaluate the model's outputs and consider potential risks before deploying it in production systems.
Things to try
Some interesting things to try with Stable LM 2 12B include:
- Experimenting with different prompting and generation strategies to explore the model's capabilities in areas like creative writing, task completion, and open-ended dialogue.
- Fine-tuning the model on domain-specific datasets to adapt it for specialized applications, such as technical writing or customer service chatbots.
- Combining the model with other AI components, such as vision models or recommender systems, to build more complex, multimodal applications.
- Investigating the model's reasoning and knowledge capabilities by probing it with a variety of questions and tasks.
As with any powerful AI system, it's important to use Stable LM 2 12B responsibly and with appropriate safeguards in place. Continuous evaluation and refinement will be crucial to ensuring the model's outputs are safe, ethical, and aligned with user needs.
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
🔮
stablelm-2-1_6b
167
Stable LM 2 1.6B is a 1.6 billion parameter decoder-only language model developed by Stability AI. It was pre-trained on 2 trillion tokens of diverse multilingual and code datasets for two epochs. This model can be contrasted with similar large language models from Stability AI, such as stable-code-3b, which is focused on code generation, and stablelm-tuned-alpha-7b, which has been fine-tuned for chat-like applications. Model inputs and outputs Stable LM 2 1.6B is a text generation model that can be used to generate natural language text based on a given prompt. The model takes a text prompt as input and outputs a continuation or completion of that text. Inputs Text prompt**: A string of text that the model will use as the starting point for text generation. Outputs Generated text**: The model will generate new text that continues or extends the input prompt. The length and content of the generated text is controlled by parameters such as max_new_tokens, temperature, and top_p. Capabilities Stable LM 2 1.6B is a powerful language model that can be used for a variety of text generation tasks, such as writing, summarization, and translation. The model has been trained on a diverse corpus of data, giving it broad knowledge and the ability to generate coherent and contextually relevant text. Some key capabilities of the model include: Multilingual generation**: The model can generate text in multiple languages, not just English. Code generation**: The model has been trained on programming language data and can generate code snippets. Creative writing**: The model can be used to generate short stories, poems, and other creative writing. What can I use it for? Stable LM 2 1.6B can be used for a variety of applications, including: Content generation**: The model can be used to generate text for blogs, articles, social media posts, and other content. Summarization**: The model can be used to summarize long passages of text. Translation**: The model's multilingual capabilities can be used for translation between languages. Prototyping and ideation**: The model can be used to generate ideas and explore creative concepts. When using Stable LM 2 1.6B commercially, please refer to the Stability AI membership information. Things to try One interesting thing to try with Stable LM 2 1.6B is its capability for generating code. By providing the model with a prompt that includes instructions for a specific coding task, you can use the model to generate working code snippets. For example, you could prompt the model with "Write a Python function to find the number of CPU cores on a system" and see the model generate a functional code solution. Another interesting aspect of the model is its ability to generate multilingual text. You can experiment with prompts in different languages to see the model's performance across various linguistic domains. This could be useful for tasks like machine translation or developing multilingual chatbots and virtual assistants. Overall, Stable LM 2 1.6B is a versatile language model with a wide range of potential applications. By exploring its various capabilities and experimenting with different prompts and use cases, you can discover new and innovative ways to leverage this powerful AI technology.
Updated Invalid Date
✅
stablelm-3b-4e1t
305
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.
Updated Invalid Date
↗️
stablelm-base-alpha-7b-v2
47
StableLM-Base-Alpha-7B-v2 is a 7 billion parameter decoder-only language model developed by Stability AI that is an improved version of the original StableLM-Base-Alpha-7B model. It was pre-trained on a diverse collection of English datasets, addressing shortcomings of the previous model through the use of better data sources and mixture ratios. Compared to the earlier StableLM-Base-Alpha models, the StableLM-Base-Alpha-7B-v2 incorporates architectural enhancements like Rotary Position Embeddings, Parallel Attention and MLP residuals, and per-head QK normalization. This allows it to outperform its predecessors in terms of language understanding and generation capabilities. Model inputs and outputs StableLM-Base-Alpha-7B-v2 is a decoder-only transformer language model, meaning it takes in a sequence of text and generates new text in an autoregressive fashion. The model can accept various types of text inputs and produce diverse outputs like informative responses, creative writing, and task-oriented instructions. Inputs Text prompts**: The model takes in natural language text prompts as input, which can range from a single sentence to multiple paragraphs. Outputs Generated text**: Based on the input prompts, the model produces new text that extends or continues the given input. The output can vary in length and style depending on the prompting. Capabilities The StableLM-Base-Alpha-7B-v2 model demonstrates impressive language understanding and generation capabilities. It can engage in open-ended conversations, answer questions, summarize information, and even generate creative content like stories and poems. The model's large 7 billion parameter size and architectural innovations allow it to capture complex linguistic patterns and generate fluent, coherent text. What can I use it for? StableLM-Base-Alpha-7B-v2 can be a valuable foundation for building a wide range of natural language processing applications. Some potential use cases include: Chatbots and virtual assistants**: The model can be fine-tuned to engage in intelligent, contextual conversations and assist users with various tasks. Content generation**: The model can be used to generate informative, creative, or task-oriented text for applications like content creation, summarization, and creative writing. Knowledge augmentation**: The model's broad training data can be leveraged to build systems that provide informative responses to queries or extract insights from text. As a base model, StableLM-Base-Alpha-7B-v2 provides a strong starting point for further fine-tuning and customization to meet specific application needs. Things to try One interesting aspect of StableLM-Base-Alpha-7B-v2 is its ability to handle long-form text inputs and generate coherent, contextual responses. Try prompting the model with a multi-paragraph passage and see how it continues the narrative or expands on the given information. Another area to explore is the model's capacity for creative writing. Provide it with a simple writing prompt, like the beginning of a short story, and observe how it generates unique and imaginative plot developments and character details. By experimenting with different types of inputs and prompts, you can uncover the model's versatility and discover new ways to leverage its language generation capabilities for your own applications.
Updated Invalid Date
🔍
stablelm-2-12b-chat
76
StableLM 2 12B Chat is a 12 billion parameter instruction-tuned language model developed by Stability AI. It is trained on a mix of publicly available datasets and synthetic datasets, utilizing Direct Preference Optimization (DPO). This model is an evolution of the Stable LM 2 12B model, with additional fine-tuning to improve its conversational abilities. Model Inputs and Outputs StableLM 2 12B Chat uses a chat-style input format, where the user prompts are presented in the user role, and the model's responses are in the assistant role. This format is available through the tokenizer's apply_chat_template method. Inputs Conversational prompts in a chat-style format, with the user's message in the user role. Outputs Responses generated by the model, in the assistant role, continuing the conversation. The model can also support function calling, allowing users to request the generation of images or other outputs. Capabilities StableLM 2 12B Chat is capable of engaging in open-ended conversations, answering questions, and generating text on a wide range of topics. It has been further fine-tuned to improve its conversational abilities compared to the base Stable LM 2 12B model. What Can I Use It For? StableLM 2 12B Chat is well-suited for building conversational AI applications, such as chatbots, virtual assistants, or dialogue systems. Its ability to understand context and engage in back-and-forth conversations makes it a powerful tool for creating more natural and engaging user experiences. Things to Try One interesting aspect of StableLM 2 12B Chat is its support for function calling, which allows users to request specific outputs beyond just text generation, such as image creation. This opens up opportunities to build more interactive and multimodal applications that seamlessly integrate language and other capabilities.
Updated Invalid Date