stable-vicuna-13B-HF
Maintainer: TheBloke
96
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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
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.
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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