exl2 quants for meow
This repository includes the quantized models for the meow model by Rishiraj Acharya. meow is a fine-tune of SOLAR-10.7B-Instruct-v1.0 with the no_robots dataset.
Current models
| exl2 BPW | Model Branch | Model Size | Minimum VRAM (4096 Context, fp16 cache) |
|---|---|---|---|
| 2-Bit | main | 3.28 GB | 6GB GPU |
| 4-Bit | 4bit | 5.61 GB | 8GB GPU |
| 5-Bit | 5bit | 6.92 GB | 10GB GPU, 8GB with swap |
| 6-Bit | 6bit | 8.23 GB | 10GB GPU |
| 8-Bit | 8bit | 10.84 GB | 12GB GPU |
Note
Using a 12GB Nvidia GeForce RTX 3060 I got on average around 20 tokens per second on the 8-bit quant in full 4096 context.
Where to use
There are a couple places you can use an exl2 model, here are a few:
- tabbyAPI
- Aphrodite Engine
- ExUI
- oobabooga's Text Gen Webui
- When using the downloader, make sure to format like this: Anthonyg5005/rishiraj-meow-10.7B-exl2:QuantBranch
- KoboldAI (Clone repo, don't use snapshot)
How to download:
oobabooga's downloader
use something like download-model.py to download with python requests.
Install requirements:
pip install requests tqdm
Example for downloading 5bpw:
python download-model.py Anthonyg5005/rishiraj-meow-10.7B-exl2:5bit
huggingface-cli
You may also use huggingface-cli
To install it, install python hf-hub
pip install huggingface-hub
Example for 5bpw:
huggingface-cli download Anthonyg5005/rishiraj-meow-10.7B-exl2 --local-dir rishiraj-meow-10.7B-exl2-5bpw --revision 5bit
Git LFS (not recommended)
I would recommend the http downloaders over using git, they can resume downloads if failed and are much easier to work with.
Make sure to have git and git LFS installed.
Example for 5bpw download with git:
Have LFS file skip disabled
# windows
set GIT_LFS_SKIP_SMUDGE=0
# linux
export GIT_LFS_SKIP_SMUDGE=0
Clone repo branch
git clone https://huggingface.co/Anthonyg5005/rishiraj-meow-10.7B-exl2 -b 5bit
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Model tree for Anthonyg5005/rishiraj-meow-10.7B-exl2
Base model
upstage/SOLAR-10.7B-v1.0