README.md

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license: apache-2.0
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<img src="pastiche-crown-clown.png" alt="Pastiche crown clown logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

# CorticalStack/pastiche-crown-clown-7b-dare-dpo-awq

CorticalStack/pastiche-crown-clown-7b-dare-dpo-awq is an AWQ quantised version of [CorticalStack/pastiche-crown-clown-7b-dare-dpo](https://huggingface.co/CorticalStack/pastiche-crown-clown-7b-dare-dpo).

### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.

It is supported by:

- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code

### AWQ configuration
- Zero point: True
- Q group size: 128
- W bit: 4
- Version: GEMM