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- en |
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## Information |
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This is a Exl2 quantized version of [Rose-20B](https://huggingface.co/tavtav/Rose-20B) |
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Please refer to the original creator for more information. |
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Calibration dataset: [wikitext](https://huggingface.co/datasets/wikitext/tree/refs%2Fconvert%2Fparquet/wikitext-2-v1/test) |
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## Branches: |
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- main: Measurement files |
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- 4bpw: 4 bits per weight |
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- 5bpw: 5 bits per weight |
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- 6bpw: 6 bits per weight |
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## Notes |
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- 6bpw is recommended for the best quality to vram usage ratio (assuming you have enough vram). |
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- Please ask for more bpws in the community tab if necessary. |
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## Run in TabbyAPI |
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TabbyAPI is a pure exllamav2 FastAPI server developed by us. You can find TabbyAPI's source code here: [https://github.com/theroyallab/TabbyAPI](https://github.com/theroyallab/TabbyAPI) |
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If you don't have huggingface-cli, please run `pip install huggingface_hub`. |
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To run this model, follow these steps: |
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1. Make a directory inside your models folder called `Rose-20B-exl2` |
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2. Open a terminal inside your models folder |
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3. Run `huggingface-cli download royallab/Rose-20B-exl2 --revision 4bpw --local-dir Rose-20B-exl2 --local-dir-use-symlinks False` |
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1. The `--revision` flag corresponds to the branch name on the model repo. Please select the appropriate bpw branch for your system. |
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4. Inside TabbyAPI's config.yml, set `model_name` to `Rose-20B-exl2` or you can use the `/model/load` endpoint after launching. |
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5. Launch TabbyAPI inside your python env by running `python main.py` |
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## Donate? |
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All my infrastructure and cloud expenses are paid out of pocket. If you'd like to donate, you can do so here: https://ko-fi.com/kingbri |
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You should not feel obligated to donate, but if you do, I'd appreciate it. |
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