stable-vicuna-13B-GGML
Maintainer: TheBloke
114
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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-GGML is a 13 billion parameter language model developed by CarperAI and quantized by TheBloke for efficient CPU and GPU inference using the GGML format. This model is based on the Vicuna language model, which was fine-tuned from the original LLaMA model to produce more helpful and engaging conversational responses.
The model is available in a variety of quantized versions, ranging from 2-bit to 8-bit, to suit different hardware and performance requirements. The 2-bit and 3-bit versions use new "k-quant" quantization methods developed by TheBloke, which aim to maintain high quality while further reducing the model size. These quantized models can run efficiently on both CPU and GPU hardware.
Similar models include June Lee's Wizard Vicuna 13B GGML and Eric Hartford's Wizard Vicuna 30B Uncensored GGML, also quantized and made available by TheBloke. These share the Vicuna architecture but differ in scale and training datasets.
Model inputs and outputs
Inputs
- Arbitrary text prompts
Outputs
- Autoregressive text generation, producing continuations of the input prompt
Capabilities
The stable-vicuna-13B-GGML model is highly capable at engaging in open-ended conversations, answering questions, and generating coherent text across a variety of domains. It can be used for tasks like chatbots, creative writing, summarization, and knowledge-intensive query answering. The model's strong performance on benchmarks like commonsense reasoning and reading comprehension suggest it has broad capabilities.
What can I use it for?
The stable-vicuna-13B-GGML model is well-suited for a variety of natural language processing tasks. It could be used to build interactive chatbots or virtual assistants, generate creative stories and articles, summarize long texts, or answer questions on a wide range of topics.
The quantized GGML versions provided by TheBloke allow for efficient deployment on both CPU and GPU hardware, making this model accessible for a range of use cases and computing environments. Developers could integrate it into applications, web services, or research projects that require high-quality language generation.
Things to try
One interesting aspect of this model is the availability of different quantization levels. Users can experiment with the trade-offs between model size, inference speed, and output quality to find the right balance for their specific needs. The new "k-quant" methods may be particularly worth exploring, as they aim to provide more efficient quantization without significant quality degradation.
Additionally, since this model is based on the Vicuna architecture, users could fine-tune it further on domain-specific data to customize its capabilities for particular applications. The model's strong performance on benchmarks suggests it has a solid foundation that could be built upon.
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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