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Check out the documentation for more information.
Quantization made by Richard Erkhov.
arco-plus - EXL2
- Model creator: https://huggingface.co/appvoid/
- Original model: https://huggingface.co/appvoid/arco-plus/
Available sizes
| Branch | Bits | Description | | ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ | | 8_0 | 8.0 | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | 6_5 | 6.5 | Very similar to 8.0, good tradeoff of size vs performance, recommended. | | 5_0 | 5.0 | Slightly lower quality vs 6.5, but usable on 8GB cards. | | 4_25 | 4.25 | GPTQ equivalent bits per weight, slightly higher quality. | | 3_5 | 3.5 | Lower quality, only use if you have to. |
Download instructions
With git:
git clone --single-branch --branch 6_5 https://huggingface.co/appvoid_-_arco-plus-exl2 arco-plus-6_5
With huggingface hub:
pip3 install huggingface-hub
To download a specific branch, use the --revision parameter. For example, to download the 6.5 bpw branch:
Linux:
huggingface-cli download appvoid_-_arco-plus-exl2 --revision 6_5 --local-dir arco-plus-6_5 --local-dir-use-symlinks False
Windows (which apparently doesn't like _ in folders sometimes?):
huggingface-cli download appvoid_-_arco-plus-exl2 --revision 6_5 --local-dir arco-plus-6.5 --local-dir-use-symlinks False
Original model description:
base_model:
- appvoid/arco
- h2oai/h2o-danube3-500m-base library_name: transformers tags:
- mergekit
- merge
arco+
This is an untrained passthrough model based on arco and danube as a first effort to train a small enough reasoning language model that generalizes across all kind of reasoning tasks.
Benchmarks
| Parameters | Model | MMLU | ARC | HellaSwag | PIQA | Winogrande | Average |
|---|---|---|---|---|---|---|---|
| 488m | arco-lite | 23.22 | 33.45 | 56.55 | 69.70 | 59.19 | 48.46 |
| 773m | arco-plus | 23.06 | 36.43 | 60.09 | 72.36 | 60.46 | 50.48 |
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: appvoid/arco
layer_range: [0, 14]
- sources:
- model: h2oai/h2o-danube3-500m-base
layer_range: [4, 16]
merge_method: passthrough
dtype: float16