| ``` | |
| e88 88e d8 | |
| d888 888b 8888 8888 ,"Y88b 888 8e d88 | |
| C8888 8888D 8888 8888 "8" 888 888 88b d88888 | |
| Y888 888P Y888 888P ,ee 888 888 888 888 | |
| "88 88" "88 88" "88 888 888 888 888 | |
| b | |
| 8b, | |
| e88'Y88 d8 888 | |
| d888 'Y ,"Y88b 888,8, d88 ,e e, 888 | |
| C8888 "8" 888 888 " d88888 d88 88b 888 | |
| Y888 ,d ,ee 888 888 888 888 , 888 | |
| "88,d88 "88 888 888 888 "YeeP" 888 | |
| PROUDLY PRESENTS | |
| ``` | |
| # 0x01-8x7b-iMat-GGUF | |
| Quantized from fp16 with love. | |
| * Quantizations made possible using .imatrix file from [this](https://huggingface.co/datasets/ikawrakow/imatrix-from-wiki-train) repo (special thanks to [ikawrakow](https://huggingface.co/ikawrakow) again) | |
| For a brief rundown of iMatrix quant performance please see this [PR](https://github.com/ggerganov/llama.cpp/pull/5747) | |
| <i>All quants are verified working prior to uploading to repo for your safety and convenience. </i> | |
| Please note importance matrix quantizations are a work in progress, IQ3 and above is recommended for best results. | |
| <b>Tip:</b> Pick a size that can fit in your GPU while still allowing some room for context for best speed. You may need to pad this further depending on if you are running image gen or TTS as well. | |
| Original model card can be found [here](https://huggingface.co/rAIfle/0x01-8x7b-hf) | |