You can deploy this model and start earning money today!

RichardErkhov/
icefog72_-_Kunokukulemonchini-7b-gguf

Name: icefog72_-_Kunokukulemonchini-7b-gguf
Availability: InStock

Views

No views yet

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Kunokukulemonchini-7b - GGUF

Model creator: https://huggingface.co/icefog72/
Original model: https://huggingface.co/icefog72/Kunokukulemonchini-7b/

Name	Quant method	Size
Kunokukulemonchini-7b.Q2_K.gguf	Q2_K	2.53GB
Kunokukulemonchini-7b.IQ3_XS.gguf	IQ3_XS	2.81GB
Kunokukulemonchini-7b.IQ3_S.gguf	IQ3_S	2.96GB
Kunokukulemonchini-7b.Q3_K_S.gguf	Q3_K_S	2.95GB
Kunokukulemonchini-7b.IQ3_M.gguf	IQ3_M	3.06GB
Kunokukulemonchini-7b.Q3_K.gguf	Q3_K	3.28GB
Kunokukulemonchini-7b.Q3_K_M.gguf	Q3_K_M	3.28GB
Kunokukulemonchini-7b.Q3_K_L.gguf	Q3_K_L	3.56GB
Kunokukulemonchini-7b.IQ4_XS.gguf	IQ4_XS	3.67GB
Kunokukulemonchini-7b.Q4_0.gguf	Q4_0	3.83GB
Kunokukulemonchini-7b.IQ4_NL.gguf	IQ4_NL	3.87GB
Kunokukulemonchini-7b.Q4_K_S.gguf	Q4_K_S	3.86GB
Kunokukulemonchini-7b.Q4_K.gguf	Q4_K	4.07GB
Kunokukulemonchini-7b.Q4_K_M.gguf	Q4_K_M	4.07GB
Kunokukulemonchini-7b.Q4_1.gguf	Q4_1	4.24GB
Kunokukulemonchini-7b.Q5_0.gguf	Q5_0	4.65GB
Kunokukulemonchini-7b.Q5_K_S.gguf	Q5_K_S	4.65GB
Kunokukulemonchini-7b.Q5_K.gguf	Q5_K	4.78GB
Kunokukulemonchini-7b.Q5_K_M.gguf	Q5_K_M	4.78GB
Kunokukulemonchini-7b.Q5_1.gguf	Q5_1	5.07GB
Kunokukulemonchini-7b.Q6_K.gguf	Q6_K	5.53GB
Kunokukulemonchini-7b.Q8_0.gguf	Q8_0	7.17GB

Original model description:

base_model:

grimjim/kukulemon-7B
Nitral-AI/Kunocchini-7b-128k-test library_name: transformers tags:
mergekit
merge
alpaca
mistral license: cc-by-nc-4.0 model-index:
name: Kunokukulemonchini-7b results:
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm value: 66.72 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/Kunokukulemonchini-7b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc_norm value: 86.31 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/Kunokukulemonchini-7b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
  - type: acc value: 65.31 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/Kunokukulemonchini-7b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 61.89 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/Kunokukulemonchini-7b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 78.45 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/Kunokukulemonchini-7b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 60.20 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/Kunokukulemonchini-7b name: Open LLM Leaderboard

Kunokukulemonchini-7b

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

Here is an 4.1bpw exl2 quant Kunokukulemonchini-7b-4.1bpw-exl2 for people like me with 6gb vram.

Thx to Natkituwu for

3.5bpw Kunokukulemonchini-7b-3.5bpw-exl2
5.0bpw Kunokukulemonchini-7b-5.0bpw-exl2
6.5bpw Kunokukulemonchini-7b-6.5bpw-exl2
7.1bpw Kunokukulemonchini-7b-7.1bpw-exl2
8.0bpw Kunokukulemonchini-7b-8.0bpw-exl2

Check out new merge model IceLemonTeaRP-32k-7b

Merge Details

Slightly edited kukulemon-7B config.json before merge to get at least ~32k context window.

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

How to download, including from branches

From the command line

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub

To download the main branch to a folder called Kunokukulemonchini-7b:

shell

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

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: grimjim/kukulemon-7B
        layer_range: [0, 32]
      - model: Nitral-AI/Kunocchini-7b-128k-test
        layer_range: [0, 32]
merge_method: slerp
base_model: Nitral-AI/Kunocchini-7b-128k-test
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
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	69.61
AI2 Reasoning Challenge (25-Shot)	66.72
HellaSwag (10-Shot)	86.31
MMLU (5-Shot)	65.31
TruthfulQA (0-shot)	61.89
Winogrande (5-shot)	78.45
GSM8k (5-shot)	60.20

RichardErkhov/icefog72_-_Kunokukulemonchini-7b-gguf