various-2bit-sota-gguf

Maintainer: ikawrakow

Total Score

79

Last updated 5/28/2024

🧠

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The various-2bit-sota-gguf model is a collection of models quantized using a new 2-bit approach developed by the maintainer ikawrakow. These models are intended for use with the llama.cpp library, which requires a specific PR to be merged. The models come in a variety of bit-width configurations, ranging from 2-bit to 8-bit, allowing tradeoffs between model size, speed, and quality. Compared to similar 2-bit models like Llama-2-7B-GGUF, the various-2bit-sota-gguf models offer improved quantization with a lower error at the expense of being slightly larger.

Model inputs and outputs

Inputs

  • Text input only

Outputs

  • Text output only

Capabilities

The various-2bit-sota-gguf models are capable of a variety of text-to-text tasks, such as natural language generation, language translation, and text summarization. Their performance will depend on the specific bit-width configuration chosen, with higher bit-widths generally offering better quality but larger model size.

What can I use it for?

The various-2bit-sota-gguf models can be used for a range of commercial and research applications that involve text generation, such as chatbots, content creation, and language modeling. The maintainer has provided GGUF versions of these models that are compatible with the llama.cpp library, as well as other popular frameworks and UIs like text-generation-webui and LangChain.

Things to try

Experiment with the different bit-width configurations to find the right balance of model size, speed, and quality for your specific use case. You can also try fine-tuning the models on your own data to further improve performance on your task of interest.



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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