Quantization made by Richard Erkhov.

Moza-7B-v1.0 - GGUF

Model creator: https://huggingface.co/kidyu/
Original model: https://huggingface.co/kidyu/Moza-7B-v1.0/

Name	Quant method	Size
Moza-7B-v1.0.Q2_K.gguf	Q2_K	2.53GB
Moza-7B-v1.0.IQ3_XS.gguf	IQ3_XS	2.81GB
Moza-7B-v1.0.IQ3_S.gguf	IQ3_S	2.96GB
Moza-7B-v1.0.Q3_K_S.gguf	Q3_K_S	2.95GB
Moza-7B-v1.0.IQ3_M.gguf	IQ3_M	3.06GB
Moza-7B-v1.0.Q3_K.gguf	Q3_K	3.28GB
Moza-7B-v1.0.Q3_K_M.gguf	Q3_K_M	3.28GB
Moza-7B-v1.0.Q3_K_L.gguf	Q3_K_L	3.56GB
Moza-7B-v1.0.IQ4_XS.gguf	IQ4_XS	3.67GB
Moza-7B-v1.0.Q4_0.gguf	Q4_0	3.83GB
Moza-7B-v1.0.IQ4_NL.gguf	IQ4_NL	3.87GB
Moza-7B-v1.0.Q4_K_S.gguf	Q4_K_S	3.86GB
Moza-7B-v1.0.Q4_K.gguf	Q4_K	4.07GB
Moza-7B-v1.0.Q4_K_M.gguf	Q4_K_M	4.07GB
Moza-7B-v1.0.Q4_1.gguf	Q4_1	4.24GB
Moza-7B-v1.0.Q5_0.gguf	Q5_0	4.65GB
Moza-7B-v1.0.Q5_K_S.gguf	Q5_K_S	4.65GB
Moza-7B-v1.0.Q5_K.gguf	Q5_K	4.78GB
Moza-7B-v1.0.Q5_K_M.gguf	Q5_K_M	4.78GB
Moza-7B-v1.0.Q5_1.gguf	Q5_1	5.07GB
Moza-7B-v1.0.Q6_K.gguf	Q6_K	5.53GB
Moza-7B-v1.0.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 library_name: transformers tags: - mergekit - merge base_model: - mistralai/Mistral-7B-v0.1 - cognitivecomputations/dolphin-2.2.1-mistral-7b - Open-Orca/Mistral-7B-OpenOrca - openchat/openchat-3.5-0106 - mlabonne/NeuralHermes-2.5-Mistral-7B - GreenNode/GreenNode-mini-7B-multilingual-v1olet - berkeley-nest/Starling-LM-7B-alpha - viethq188/LeoScorpius-7B-Chat-DPO - meta-math/MetaMath-Mistral-7B - Intel/neural-chat-7b-v3-3 inference: false model-index: - name: Moza-7B-v1.0 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: 66.55 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kidyu/Moza-7B-v1.0 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: 83.45 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kidyu/Moza-7B-v1.0 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: 62.77 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kidyu/Moza-7B-v1.0 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: 65.16 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kidyu/Moza-7B-v1.0 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: 77.51 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kidyu/Moza-7B-v1.0 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: 62.55 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kidyu/Moza-7B-v1.0 name: Open LLM Leaderboard

Moza-7B-v1.0

This is a meme-merge of pre-trained language models, created using mergekit. Use at your own risk.

Details

Quantized Model

GGUF

Merge Method

This model was merged using the DARE TIES merge method, using mistralai/Mistral-7B-v0.1 as a base.

The value for density are from this blogpost, and the weight was randomly generated and then assigned to the models, with priority (of using the bigger weight) to NeuralHermes, OpenOrca, and neural-chat. The models themselves are chosen by "vibes".

Models Merged

The following models were included in the merge:

Prompt Format

You can use Alpaca formatting for inference

### Instruction:

### Response:

Configuration

The following YAML configuration was used to produce this model:

base_model: mistralai/Mistral-7B-v0.1
models:
  - model: mlabonne/NeuralHermes-2.5-Mistral-7B
    parameters:
      density: 0.63
      weight: 0.83
  - model: Intel/neural-chat-7b-v3-3
    parameters:
      density: 0.63
      weight: 0.74
  - model: meta-math/MetaMath-Mistral-7B
    parameters:
      density: 0.63
      weight: 0.22
  - model: openchat/openchat-3.5-0106
    parameters:
      density: 0.63
      weight: 0.37
  - model: Open-Orca/Mistral-7B-OpenOrca
    parameters:
      density: 0.63
      weight: 0.76
  - model: cognitivecomputations/dolphin-2.2.1-mistral-7b
    parameters:
      density: 0.63
      weight: 0.69
  - model: viethq188/LeoScorpius-7B-Chat-DPO
    parameters:
      density: 0.63
      weight: 0.38
  - model: GreenNode/GreenNode-mini-7B-multilingual-v1olet
    parameters:
      density: 0.63
      weight: 0.13
  - model: berkeley-nest/Starling-LM-7B-alpha
    parameters:
      density: 0.63
      weight: 0.33
merge_method: dare_ties
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	69.66
AI2 Reasoning Challenge (25-Shot)	66.55
HellaSwag (10-Shot)	83.45
MMLU (5-Shot)	62.77
TruthfulQA (0-shot)	65.16
Winogrande (5-shot)	77.51
GSM8k (5-shot)	62.55