stablelm-tuned-alpha-7b
Maintainer: stability-ai
110
| Property | Value |
|---|---|
| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | No paper link provided |
Get summaries of the top AI models delivered straight to your inbox:
Model overview
StableLM-Tuned-Alpha is a suite of 3B and 7B parameter decoder-only language models developed by Stability AI. These models are built on top of the StableLM-Base-Alpha models and further fine-tuned on various chat and instruction-following datasets. The models are capable of generating coherent and context-aware text, making them useful for a variety of language-based applications.
Similar models developed by Stability AI include stable-diffusion, a latent text-to-image diffusion model, and japanese-stable-diffusion-xl, a version of Stable Diffusion fine-tuned on Japanese data. Another related model is japanese-stablelm-base-alpha-7b, a 7B-parameter decoder-only language model pre-trained on a diverse collection of Japanese and English datasets.
Model inputs and outputs
StableLM-Tuned-Alpha is a generative language model that can be used to produce human-like text based on a given prompt. The model takes in a text prompt as input and generates a continuation of the text, with the length of the output controlled by the max_tokens parameter.
Inputs
- Prompt: The initial text that the model will use to generate a continuation.
- Max Tokens: The maximum number of tokens (roughly equivalent to words) to generate.
- Temperature: A parameter that controls the randomness of the generated text, with higher values resulting in more diverse and unpredictable output.
- Top P: A parameter that controls the diversity of the generated text by limiting the model to sampling from the top P% most likely tokens.
- Repetition Penalty: A parameter that discourages the model from repeating the same words or phrases in the generated text.
Outputs
- Generated Text: The continuation of the input prompt, generated by the model.
Capabilities
StableLM-Tuned-Alpha can be used for a variety of language-based tasks, such as chatbots, creative writing, and question answering. The model's fine-tuning on datasets like Alpaca, GPT4All, and ShareGPT Vicuna gives it the ability to engage in helpful and contextual conversations, as well as follow instructions and generate creative content.
What can I use it for?
StableLM-Tuned-Alpha can be used to build chatbot applications, where the model can engage in natural conversations with users and provide helpful information or responses. The model's versatility also allows it to be used for creative writing tasks, such as generating short stories, poems, or even comedy sketches.
Additionally, the model's ability to follow instructions and answer questions makes it potentially useful for educational applications, where it could be used to help students with research, analysis, or even homework assignments.
Things to try
One interesting aspect of StableLM-Tuned-Alpha is its ability to write poetry and make jokes, as mentioned in the model's description. Users could experiment with prompts that encourage the model to generate creative content, such as "Write a haiku about the changing seasons" or "Tell me your best joke."
Another interesting direction to explore would be the model's potential for task-following and instruction-following. Users could try giving the model more complex prompts that involve multiple steps or specific instructions, and see how well it can understand and execute those tasks.
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
stable-diffusion
107.9K
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. Developed by Stability AI, it is an impressive AI model that can create stunning visuals from simple text prompts. The model has several versions, with each newer version being trained for longer and producing higher-quality images than the previous ones. The main advantage of Stable Diffusion is its ability to generate highly detailed and realistic images from a wide range of textual descriptions. This makes it a powerful tool for creative applications, allowing users to visualize their ideas and concepts in a photorealistic way. The model has been trained on a large and diverse dataset, enabling it to handle a broad spectrum of subjects and styles. Model inputs and outputs Inputs Prompt**: The text prompt that describes the desired image. This can be a simple description or a more detailed, creative prompt. Seed**: An optional random seed value to control the randomness of the image generation process. Width and Height**: The desired dimensions of the generated image, which must be multiples of 64. Scheduler**: The algorithm used to generate the image, with options like DPMSolverMultistep. Num Outputs**: The number of images to generate (up to 4). Guidance Scale**: The scale for classifier-free guidance, which controls the trade-off between image quality and faithfulness to the input prompt. Negative Prompt**: Text that specifies things the model should avoid including in the generated image. Num Inference Steps**: The number of denoising steps to perform during the image generation process. Outputs Array of image URLs**: The generated images are returned as an array of URLs pointing to the created images. Capabilities Stable Diffusion is capable of generating a wide variety of photorealistic images from text prompts. It can create images of people, animals, landscapes, architecture, and more, with a high level of detail and accuracy. The model is particularly skilled at rendering complex scenes and capturing the essence of the input prompt. One of the key strengths of Stable Diffusion is its ability to handle diverse prompts, from simple descriptions to more creative and imaginative ideas. The model can generate images of fantastical creatures, surreal landscapes, and even abstract concepts with impressive results. What can I use it for? Stable Diffusion can be used for a variety of creative applications, such as: Visualizing ideas and concepts for art, design, or storytelling Generating images for use in marketing, advertising, or social media Aiding in the development of games, movies, or other visual media Exploring and experimenting with new ideas and artistic styles The model's versatility and high-quality output make it a valuable tool for anyone looking to bring their ideas to life through visual art. By combining the power of AI with human creativity, Stable Diffusion opens up new possibilities for visual expression and innovation. Things to try One interesting aspect of Stable Diffusion is its ability to generate images with a high level of detail and realism. Users can experiment with prompts that combine specific elements, such as "a steam-powered robot exploring a lush, alien jungle," to see how the model handles complex and imaginative scenes. Additionally, the model's support for different image sizes and resolutions allows users to explore the limits of its capabilities. By generating images at various scales, users can see how the model handles the level of detail and complexity required for different use cases, such as high-resolution artwork or smaller social media graphics. Overall, Stable Diffusion is a powerful and versatile AI model that offers endless possibilities for creative expression and exploration. By experimenting with different prompts, settings, and output formats, users can unlock the full potential of this cutting-edge text-to-image technology.
Updated Invalid Date
sdxl
51.1K
sdxl is a text-to-image generative AI model created by Stability AI, the same company behind the popular Stable Diffusion model. Like Stable Diffusion, sdxl can generate beautiful, photorealistic images from text prompts. However, sdxl has been designed to create even higher-quality images with additional capabilities such as inpainting and image refinement. Model inputs and outputs sdxl takes a variety of inputs to generate and refine images, including text prompts, existing images, and masks. The model can output multiple images per input, allowing users to explore different variations. The specific inputs and outputs are: Inputs Prompt**: A text description of the desired image Negative Prompt**: Text that specifies elements to exclude from the image Image**: An existing image to use as a starting point for img2img or inpainting Mask**: A black and white image indicating which parts of the input image should be preserved or inpainted Seed**: A random number to control the image generation process Refine**: The type of refinement to apply to the generated image Scheduler**: The algorithm used to generate the image Guidance Scale**: The strength of the text guidance during image generation Num Inference Steps**: The number of denoising steps to perform during generation Lora Scale**: The additive scale for any LoRA (Low-Rank Adaptation) weights used Refine Steps**: The number of refinement steps to perform (for certain refinement methods) High Noise Frac**: The fraction of noise to use (for certain refinement methods) Apply Watermark**: Whether to apply a watermark to the generated image Outputs One or more generated images, returned as image URLs Capabilities sdxl can generate a wide range of high-quality images from text prompts, including scenes, objects, and creative visualizations. The model also supports inpainting, where you can provide an existing image and a mask, and sdxl will fill in the masked areas with new content. Additionally, sdxl offers several refinement options to further improve the generated images. What can I use it for? sdxl is a versatile model that can be used for a variety of creative and commercial applications. For example, you could use it to: Generate concept art or illustrations for games, books, or other media Create custom product images or visualizations for e-commerce or marketing Produce unique, personalized art and design assets Experiment with different artistic styles and visual ideas Things to try One interesting aspect of sdxl is its ability to refine and enhance generated images. You can try using different refinement methods, such as the base_image_refiner or expert_ensemble_refiner, to see how they affect the output quality and style. Additionally, you can play with the Lora Scale parameter to adjust the influence of any LoRA weights used by the model.
Updated Invalid Date
👁️
japanese-stable-diffusion-xl
80
japanese-stable-diffusion-xl is a text-to-image AI model developed by Stability AI. It builds upon the Stable Diffusion model, which has revolutionized the field of AI-generated art. While details about the specific model and its training process are not provided, it can be inferred that japanese-stable-diffusion-xl likely focuses on improving text-to-image generation for the Japanese language and cultural context. Similar models include stable-diffusion-inpainting, which can fill in masked parts of images using Stable Diffusion, and Taiyi-Stable-Diffusion-XL-3.5B, which aims to enhance Chinese text-to-image generation while retaining English proficiency. Model inputs and outputs Inputs Text prompts that describe the desired image Outputs AI-generated images that match the provided text prompts Capabilities japanese-stable-diffusion-xl is capable of generating high-quality, photorealistic images from text prompts. While the specific details of its capabilities are not provided, it can be inferred that the model is optimized for Japanese language and cultural contexts, potentially producing images that reflect Japanese aesthetics and subject matter. What can I use it for? japanese-stable-diffusion-xl can be used for a variety of creative applications, such as generating illustrations, concept art, and other visual content inspired by Japanese culture and themes. This model could be particularly useful for Japanese-focused projects, such as anime, manga, or video game development, where the ability to produce authentic-looking Japanese-inspired imagery is valuable. Things to try Experiment with different types of Japanese-themed text prompts to see the range of images the model can generate. Try prompts that incorporate Japanese cultural elements, such as traditional clothing, architecture, landscapes, or mythological creatures. Additionally, you could explore the model's capabilities in generating images that blend Japanese and Western influences, or that explore the intersection of Japanese and global pop culture.
Updated Invalid Date
starling-lm-7b-alpha
35
The starling-lm-7b-alpha is an open large language model (LLM) developed by berkeley-nest and trained using Reinforcement Learning from AI Feedback (RLAIF). The model is built upon the Openchat 3.5 base model and uses the berkeley-nest/Starling-RM-7B-alpha reward model and the advantage-induced policy alignment (APA) policy optimization method. The starling-lm-7b-alpha model scores 8.09 on the MT Bench benchmark, outperforming many other LLMs except for OpenAI's GPT-4 and GPT-4 Turbo. Similar models include the Starling-LM-7B-beta which uses an upgraded reward model and policy optimization technique, as well as stable-diffusion and stablelm-tuned-alpha-7b from Stability AI. Model inputs and outputs Inputs prompt**: The text prompt to send to the model. max_tokens**: The maximum number of tokens to generate per output sequence. temperature**: A float that controls the randomness of the sampling, with lower values making the model more deterministic and higher values making it more random. top_k**: An integer that controls the number of top tokens to consider during generation. top_p**: A float that controls the cumulative probability of the top tokens to consider, with values between 0 and 1. presence_penalty**: A float that penalizes new tokens based on whether they appear in the generated text so far, with values greater than 0 encouraging the use of new tokens and values less than 0 encouraging token repetition. frequency_penalty**: A float that penalizes new tokens based on their frequency in the generated text so far, with values greater than 0 encouraging the use of new tokens and values less than 0 encouraging token repetition. stop**: A list of strings that, when generated, will stop the generation process. Outputs Output**: A string containing the generated text. Capabilities The starling-lm-7b-alpha model is capable of generating high-quality text on a wide range of topics, outperforming many other LLMs on benchmark tasks. It can be used for tasks such as language translation, question answering, and creative writing, among others. What can I use it for? The starling-lm-7b-alpha model can be used for a variety of natural language processing tasks, such as: Content Generation**: The model can be used to generate high-quality text for articles, stories, or other types of content. Language Translation**: The model can be fine-tuned for language translation tasks, allowing it to translate text between different languages. Question Answering**: The model can be used to answer a wide range of questions on various topics. Chatbots and Conversational AI**: The model can be used to build conversational AI applications, such as virtual assistants or chatbots. The model is hosted on the LMSYS Chatbot Arena platform, allowing users to test and experiment with the model for free. Things to try One interesting aspect of the starling-lm-7b-alpha model is its ability to generate text with a high degree of coherence and consistency. By adjusting the temperature and other generation parameters, users can experiment with the model's creativity and expressiveness, while still maintaining a clear and logical narrative flow. Additionally, the model's strong performance on benchmark tasks suggests it could be a valuable tool for a wide range of natural language processing applications. Users may want to explore fine-tuning the model for specific domains or tasks, or integrating it into larger AI systems to leverage its capabilities.
Updated Invalid Date