Quantization made by Richard Erkhov.

CosmicBun-8B - GGUF

Model creator: https://huggingface.co/aloobun/
Original model: https://huggingface.co/aloobun/CosmicBun-8B/

Name	Quant method	Size
CosmicBun-8B.Q2_K.gguf	Q2_K	2.96GB
CosmicBun-8B.IQ3_XS.gguf	IQ3_XS	3.28GB
CosmicBun-8B.IQ3_S.gguf	IQ3_S	3.43GB
CosmicBun-8B.Q3_K_S.gguf	Q3_K_S	3.41GB
CosmicBun-8B.IQ3_M.gguf	IQ3_M	3.52GB
CosmicBun-8B.Q3_K.gguf	Q3_K	3.74GB
CosmicBun-8B.Q3_K_M.gguf	Q3_K_M	3.74GB
CosmicBun-8B.Q3_K_L.gguf	Q3_K_L	4.03GB
CosmicBun-8B.IQ4_XS.gguf	IQ4_XS	4.18GB
CosmicBun-8B.Q4_0.gguf	Q4_0	4.34GB
CosmicBun-8B.IQ4_NL.gguf	IQ4_NL	4.38GB
CosmicBun-8B.Q4_K_S.gguf	Q4_K_S	4.37GB
CosmicBun-8B.Q4_K.gguf	Q4_K	4.58GB
CosmicBun-8B.Q4_K_M.gguf	Q4_K_M	4.58GB
CosmicBun-8B.Q4_1.gguf	Q4_1	4.78GB
CosmicBun-8B.Q5_0.gguf	Q5_0	5.21GB
CosmicBun-8B.Q5_K_S.gguf	Q5_K_S	5.21GB
CosmicBun-8B.Q5_K.gguf	Q5_K	5.34GB
CosmicBun-8B.Q5_K_M.gguf	Q5_K_M	5.34GB
CosmicBun-8B.Q5_1.gguf	Q5_1	5.65GB
CosmicBun-8B.Q6_K.gguf	Q6_K	6.14GB
CosmicBun-8B.Q8_0.gguf	Q8_0	7.95GB

Original model description:

license: mit library_name: transformers tags: - mergekit - merge - math - llama3 - physics - chemistry - biology - dolphin base_model: - cognitivecomputations/dolphin-2.9-llama3-8b - Weyaxi/Einstein-v6.1-Llama3-8B - Locutusque/llama-3-neural-chat-v1-8b model-index: - name: CosmicBun-8B 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: 61.86 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/CosmicBun-8B 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: 84.29 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/CosmicBun-8B 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: 65.53 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/CosmicBun-8B 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: 54.08 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/CosmicBun-8B 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: 78.85 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/CosmicBun-8B 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: 68.23 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/CosmicBun-8B name: Open LLM Leaderboard

model

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

Merge Method

This model was merged using the DARE TIES merge method using Locutusque/llama-3-neural-chat-v1-8b as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: Locutusque/llama-3-neural-chat-v1-8b
dtype: bfloat16
merge_method: dare_ties
parameters:
  int8_mask: 1.0
  normalize: 0.0
slices:
- sources:
  - layer_range: [0, 4]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 1.0
      weight: 0.6
  - layer_range: [0, 4]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 0.6
      weight: 0.5
  - layer_range: [0, 4]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 1.0
      weight: 0.5
- sources:
  - layer_range: [4, 8]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 0.8
      weight: 0.1
  - layer_range: [4, 8]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 1.0
      weight: 0.2
  - layer_range: [4, 8]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 1.0
      weight: 0.7
- sources:
  - layer_range: [8, 12]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 0.7
      weight: 0.1
  - layer_range: [8, 12]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 0.7
      weight: 0.2
  - layer_range: [8, 12]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 0.7
      weight: 0.6
- sources:
  - layer_range: [12, 16]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 0.9
      weight: 0.2
  - layer_range: [12, 16]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 0.6
      weight: 0.6
  - layer_range: [12, 16]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 0.7
      weight: 0.3
- sources:
  - layer_range: [16, 20]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 1.0
      weight: 0.2
  - layer_range: [16, 20]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 1.0
      weight: 0.2
  - layer_range: [16, 20]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 0.9
      weight: 0.4
- sources:
  - layer_range: [20, 24]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 0.7
      weight: 0.2
  - layer_range: [20, 24]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 0.9
      weight: 0.3
  - layer_range: [20, 24]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 1.0
      weight: 0.4
- sources:
  - layer_range: [24, 28]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 1.0
      weight: 0.4
  - layer_range: [24, 28]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 0.8
      weight: 0.2
  - layer_range: [24, 28]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 0.9
      weight: 0.4
- sources:
  - layer_range: [28, 32]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 1.0
      weight: 0.3
  - layer_range: [28, 32]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 0.9
      weight: 0.2
  - layer_range: [28, 32]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 1.0
      weight: 0.3

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	68.81
AI2 Reasoning Challenge (25-Shot)	61.86
HellaSwag (10-Shot)	84.29
MMLU (5-Shot)	65.53
TruthfulQA (0-shot)	54.08
Winogrande (5-shot)	78.85
GSM8k (5-shot)	68.23