vicuna-13b-4bit
Maintainer: elinas
46
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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
The vicuna-13b-4bit model is a compressed version of the Vicuna 13B model, optimized for performance using the GPTQ 4-bit quantization technique. Vicuna is a high-coherence language model based on the LLaMA architecture, comparable to ChatGPT in capability. The model was created by elinas at Hugging Face.
Similar models include the llama-7b-hf-transformers-4.29 and alpaca-30b-lora-int4 models, which are also based on the LLaMA architecture and optimized for performance using quantization techniques.
Model inputs and outputs
Inputs
- Prompt: A text prompt that the model will use to generate a response.
Outputs
- Generated text: The model will generate a response based on the input prompt. The response will be coherent and relevant to the prompt.
Capabilities
The vicuna-13b-4bit model is capable of engaging in open-ended dialogue, answering questions, and generating human-like text on a variety of topics. It has been trained on a large corpus of text data and can draw upon this knowledge to provide informative and engaging responses.
What can I use it for?
The vicuna-13b-4bit model can be used for a variety of applications, such as building chatbots, generating creative writing, and answering questions. The model's compressed size and optimized performance make it well-suited for deployment on resource-constrained devices or in scenarios where real-time response is important.
Things to try
One interesting thing to try with the vicuna-13b-4bit model is to provide it with prompts that require reasoning or logical thinking. For example, you could ask the model to solve a math problem or provide an analysis of a complex topic. The model's strong performance on benchmarks like MMLU suggests that it may be capable of more advanced reasoning tasks.
Another interesting avenue to explore is using the model in a collaborative setting, where users can engage in back-and-forth conversations and build upon each other's ideas. The model's ability to maintain coherence and context over multiple exchanges could make it a valuable tool for brainstorming or ideation.
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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