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RichardErkhov/
s3nh_-_Mistral_Sonyichi-7B-slerp-gguf

Name: s3nh_-_Mistral_Sonyichi-7B-slerp-gguf
Availability: InStock

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Mistral_Sonyichi-7B-slerp - GGUF

Model creator: https://huggingface.co/s3nh/
Original model: https://huggingface.co/s3nh/Mistral_Sonyichi-7B-slerp/

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

Original model description:

language:

en license: openrail library_name: transformers tags:
mergekit
merge base_model:
SanjiWatsuki/Sonya-7B
EmbeddedLLM/Mistral-7B-Merge-14-v0.1
SanjiWatsuki/Kunoichi-7B pipeline_tag: text-generation model-index:
name: Mistral_Sonyichi-7B-slerp 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: 67.49 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=s3nh/Mistral_Sonyichi-7B-slerp 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: 86.43 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=s3nh/Mistral_Sonyichi-7B-slerp 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: 63.58 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=s3nh/Mistral_Sonyichi-7B-slerp 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: 63.25 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=s3nh/Mistral_Sonyichi-7B-slerp 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: 78.53 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=s3nh/Mistral_Sonyichi-7B-slerp 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: 63.84 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=s3nh/Mistral_Sonyichi-7B-slerp name: Open LLM Leaderboard

merged

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

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

yaml

base_model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1
dtype: bfloat16
merge_method: slerp
parameters:
  t:
  - filter: self_attn
    value: [0.0, 0.5, 0.3, 0.7, 1.0]
  - filter: mlp
    value: [1.0, 0.5, 0.7, 0.3, 0.0]
  - value: 0.5
slices:
- sources:
  - layer_range: [0, 32]
    model: SanjiWatsuki/Sonya-7B
  - layer_range: [0, 32]
    model: SanjiWatsuki/Kunoichi-7B

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	70.52
AI2 Reasoning Challenge (25-Shot)	67.49
HellaSwag (10-Shot)	86.43
MMLU (5-Shot)	63.58
TruthfulQA (0-shot)	63.25
Winogrande (5-shot)	78.53
GSM8k (5-shot)	63.84

RichardErkhov/s3nh_-_Mistral_Sonyichi-7B-slerp-gguf