IceSakeRP-7b (IceSakeV12)

This is a merge of pre-trained language models created using mergekit.

The model should handle 25-32k context window size.

ST Discord thread of model - feedback

Rules-lorebook and settings I'm using you can find here ('By model' folder)

ko-fi To buy sweets for my cat :3

Exl2 Quants

• 4.2bpw-exl2
• 6.5bpw-exl2
• 8bpw-exl2

Thx mradermacher for GGUF

• GGUF
• i1-GGUF

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

All before last one (bfloat16)

• 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:

mkdir IceSakeRP-7b
huggingface-cli download icefog72/IceSakeRP-7b --local-dir IceSakeRP-7b --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:

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:

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

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	21.44
IFEval (0-Shot)	52.13
BBH (3-Shot)	31.65
MATH Lvl 5 (4-Shot)	5.82
GPQA (0-shot)	4.70
MuSR (0-shot)	10.23
MMLU-PRO (5-shot)	24.13