Instructions to use ArtusDev/baichuan-inc_Baichuan-M2-32B-EXL3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArtusDev/baichuan-inc_Baichuan-M2-32B-EXL3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ArtusDev/baichuan-inc_Baichuan-M2-32B-EXL3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ArtusDev/baichuan-inc_Baichuan-M2-32B-EXL3: direct link, hf CLI and curl.
- Browser
- Download file 1.75 kB
-
https://huggingface.co/ArtusDev/baichuan-inc_Baichuan-M2-32B-EXL3/resolve/d2d0683cbb82353bb3d23bbf96fae90866164170/README.md
- Command line
-
hf download hf://ArtusDev/baichuan-inc_Baichuan-M2-32B-EXL3@d2d0683cbb82353bb3d23bbf96fae90866164170/README.md
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curl -L -o README.md https://huggingface.co/ArtusDev/baichuan-inc_Baichuan-M2-32B-EXL3/resolve/d2d0683cbb82353bb3d23bbf96fae90866164170/README.md
1.75 kB
metadata
base_model: baichuan-inc/Baichuan-M2-32B
base_model_relation: quantized
quantized_by: ArtusDev
license: apache-2.0
tags:
- chat
- exl3
library_name: transformers
language:
- en
- zh
EXL3 Quants of baichuan-inc/Baichuan-M2-32B
EXL3 quants of baichuan-inc/Baichuan-M2-32B using exllamav3 for quantization.
Quants
| Quant(Revision) | Bits per Weight | Head Bits |
|---|---|---|
| 2.5_H6 | 2.5 | 6 |
| 3.0_H6 | 3.0 | 6 |
| 3.5_H6 | 3.5 | 6 |
| 4.0_H6 | 4.0 | 6 |
| 4.5_H6 | 4.5 | 6 |
| 5.0_H6 | 5.0 | 6 |
| 6.0_H6 | 6.0 | 6 |
| 8.0_H8 | 8.0 | 8 |
Downloading quants with huggingface-cli
Click to view download instructions
Install hugginface-cli:
pip install -U "huggingface_hub[cli]"
Download quant by targeting the specific quant revision (branch):
huggingface-cli download ArtusDev/baichuan-inc_Baichuan-M2-32B-EXL3 --revision "5.0bpw_H6" --local-dir ./