stable-vicuna-13b-delta
Maintainer: CarperAI
458
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| 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 |
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Model Overview
StableVicuna-13B is a language model fine-tuned from the LLaMA transformer architecture. It was developed by Duy Phung of CarperAI using reinforcement learning from human feedback (RLHF) via Proximal Policy Optimization (PPO). The model was trained on a mix of datasets, including the OpenAssistant Conversations Dataset (OASST1), GPT4All Prompt Generations, and Alpaca.
Similar AI models include stable-vicuna-13B-HF and stable-vicuna-13B-GGML developed by TheBloke, which provide quantized and optimized versions of the original StableVicuna-13B model.
Model Inputs and Outputs
Inputs
- Text prompts for generation tasks
Outputs
- Generated text based on the input prompts
Capabilities
StableVicuna-13B is capable of engaging in open-ended conversations, answering questions, and generating text on a variety of topics. It has been fine-tuned to provide more stable and coherent responses compared to the base LLaMA model.
What Can I Use It For?
StableVicuna-13B can be used for a range of text generation tasks, such as chatbots, content creation, question answering, and creative writing. Due to its conversational abilities, it may be particularly useful for building interactive AI assistants. Users can further fine-tune the model on their own data to improve performance on specific tasks.
Things to Try
Experiment with the model's conversational abilities by providing it with open-ended prompts and see how it responds. You can also try using the model for creative writing exercises, such as generating short stories or poems. Additionally, consider fine-tuning the model on your own data to adapt it to your specific use case.
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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stable-vicuna-13B-HF is an unquantized float16 model of CarperAI's StableVicuna 13B, which was fine-tuned using reinforcement learning from human feedback (RLHF) via Proximal Policy Optimization (PPO) on various conversational and instructional datasets. It is the result of merging the deltas from the above repository with the original LLaMA 13B weights. TheBloke provides this model in multiple quantized versions for efficient inference, including 4-bit GPTQ models and 2-8 bit GGML models. Model inputs and outputs stable-vicuna-13B-HF is a text-to-text generative language model that can be used for a variety of natural language tasks. It takes text prompts as input and generates continued text as output. Inputs Text prompts of variable length Outputs Continued text generated in response to the input prompt The model can generate long-form text, engage in conversations, and complete a variety of language tasks Capabilities stable-vicuna-13B-HF is capable of engaging in open-ended conversations, answering questions, summarizing text, and completing a wide range of language-based tasks. It demonstrates strong performance on benchmarks compared to prior language models like VicunaLM. The model's conversational and task-completion abilities make it useful for applications like virtual assistants, content generation, and language learning. What can I use it for? stable-vicuna-13B-HF can be used for a variety of applications that require natural language understanding and generation, such as: Building virtual assistants and chatbots Generating creative content like stories, articles, and scripts Providing language learning and practice tools Summarizing and analyzing text Answering questions and providing information on a wide range of topics The model's flexibility and strong performance make it a compelling option for those looking to leverage large language models in their projects. Things to try One interesting aspect of stable-vicuna-13B-HF is its ability to engage in multi-turn conversations and maintain context over extended interactions. Try prompting the model with a conversational thread and see how it responds and builds upon the dialogue. You can also experiment with using the model for more specialized tasks, like code generation or task planning, to explore the breadth of its capabilities.
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vicuna-13b-delta-v0
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The vicuna-13b-delta-v0 is a chat assistant model developed by LMSYS. It is fine-tuned from the LLaMA language model with supervised instruction on user-shared conversations collected from ShareGPT. The model is available in different versions, including the vicuna-7b-delta-v0, vicuna-13b-v1.1, vicuna-7b-v1.3, and vicuna-33b-v1.3, each with its own unique training details and performance characteristics. These models are intended for research on large language models and chatbots, and are targeted at researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. Model inputs and outputs The vicuna-13b-delta-v0 model is an auto-regressive language model that takes in text as input and generates additional text as output. The model can be used for a variety of natural language processing tasks, such as text generation, conversation, and question answering. Inputs Text prompts that the model can use to generate additional text. Outputs Coherent and contextually relevant text generated in response to the input prompts. Capabilities The vicuna-13b-delta-v0 model has been trained on a large corpus of conversational data and can engage in natural and engaging dialogue. It demonstrates strong capabilities in tasks such as open-ended conversation, task-oriented dialogue, and providing informative and helpful responses to a wide range of queries. What can I use it for? The primary use of the vicuna-13b-delta-v0 model is for research on large language models and chatbots. Researchers and hobbyists in natural language processing, machine learning, and artificial intelligence can use the model to explore topics such as language generation, dialogue systems, and the societal impacts of AI. The model could also be used as a starting point for developing custom chatbots or virtual assistants for specific applications or domains. Things to try Researchers and hobbyists can experiment with the vicuna-13b-delta-v0 model to explore its capabilities in areas such as task-oriented dialogue, open-ended conversation, and knowledge-intensive question answering. Additionally, they can fine-tune the model on domain-specific data to adapt it for specialized applications, or use it as a starting point for developing more advanced chatbots or virtual assistants.
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vicuna-13b-delta-v1.1
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vicuna-13b-delta-v1.1 is a large language model developed by LMSYS. It is fine-tuned from the LLaMA model and trained on user-shared conversations collected from ShareGPT. This "delta model" cannot be used directly, but rather must be applied on top of the original LLaMA weights to get the actual Vicuna weights. Similar models include vicuna-13b-delta-v0, vicuna-7b-delta-v0, vicuna-13b-v1.1, and vicuna-7b-v1.3. Model inputs and outputs vicuna-13b-delta-v1.1 is an auto-regressive language model that takes in text and generates new text. It can be used for a variety of natural language processing tasks such as text generation, question answering, and conversational AI. Inputs Text prompts Outputs Generated text Capabilities vicuna-13b-delta-v1.1 has been trained to engage in open-ended dialogue and assist with a wide range of tasks. It demonstrates strong language understanding and generation capabilities, allowing it to provide informative and coherent responses. The model can be used for research on large language models and chatbots. What can I use it for? The primary use of vicuna-13b-delta-v1.1 is for research on large language models and chatbots. Researchers and hobbyists in natural language processing, machine learning, and artificial intelligence can use the model to explore advancements in these fields. To get started, users can access the model through the command line interface or APIs provided by the maintainer. Things to try Experiment with the model's language generation capabilities by providing it with a variety of prompts and observing the outputs. Assess the model's performance on natural language tasks and compare it to other language models. Explore ways to fine-tune or adapt the model for specific applications or domains.
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