IceSakeV11_1
- IceCocoaRP-7b
- IceSakeV8RP-7b (base_model)
IceSakeV11_2 (base_model)
- IceSakeV6RP-7b
- IceSakeV0RP-7b (base_model)
  - IceCocoaRP-7b (base_model)
  - IceKunoichiRP-7b
    - KunoichiVerse-7B (base_model)
    - daybreak-kunoichi-2dpo-7b

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub

To download the main branch to a folder called IceSakeRP-7b-8bpw-exl2:

shell

mkdir IceSakeRP-7b-8bpw-exl2
huggingface-cli download icefog72/IceSakeRP-7b-8bpw-exl2 --local-dir IceSakeRP-7b-8bpw-exl2 --local-dir-use-symlinks False

More advanced huggingface-cli download usage

If you remove the --local-dir-use-symlinks False parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: ~/.cache/huggingface), and symlinks will be added to the specified --local-dir, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.

The cache location can be changed with the HF_HOME environment variable, and/or the --cache-dir parameter to huggingface-cli.

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install hf_transfer

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

shell

mkdir FOLDERNAME
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MODEL --local-dir FOLDERNAME --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.

Configuration

The following YAML configuration was used to produce this model:

yaml

slices:
  - sources:
      - model: IceSakeV11_1
        layer_range: [0, 32]
      - model: IceSakeV11_2
        layer_range: [0, 32]

merge_method: slerp
base_model: IceSakeV11_2
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5 # fallback for rest of tensors

dtype: float16