stable-vicuna-13B-GPTQ
Maintainer: TheBloke
218
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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 stable-vicuna-13B-GPTQ is a quantized version of CarperAI's StableVicuna 13B model, created by TheBloke. It was produced by merging the deltas from the CarperAI repository with the original LLaMA 13B weights, then quantizing the model to 4-bit using the GPTQ-for-LLaMa tool. This allows for more efficient inference on GPU hardware compared to the full-precision model.
TheBloke also provides GGML format models for CPU and GPU inference, as well as an unquantized float16 model for further fine-tuning.
Model inputs and outputs
Inputs
- Text prompts, which can be in the format:
### Human: your prompt here ### Assistant:
Outputs
- Fluent, coherent text responses to the provided prompts, generated in an autoregressive manner.
Capabilities
The stable-vicuna-13B-GPTQ model is capable of engaging in open-ended conversational tasks, answering questions, and generating text on a wide variety of subjects. It has been trained using reinforcement learning from human feedback (RLHF) to improve its safety and helpfulness.
What can I use it for?
The stable-vicuna-13B-GPTQ model could be used for projects requiring a capable and flexible language model, such as chatbots, question-answering systems, text generation, and more. The quantized nature of the model allows for efficient inference on GPU hardware, making it suitable for real-time applications.
Things to try
One interesting thing to try with the stable-vicuna-13B-GPTQ model is using it as a starting point for further fine-tuning on domain-specific datasets. The unquantized float16 model provided by TheBloke would be well-suited for this purpose, as the quantization process can sometimes reduce the model's performance on certain tasks.
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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