RichardErkhov/nlpguy_-_ColorShadow-7B-v2-gguf

Quantization made by Richard Erkhov.

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ColorShadow-7B-v2 - GGUF

• Model creator: https://huggingface.co/nlpguy/
• Original model: https://huggingface.co/nlpguy/ColorShadow-7B-v2/

Name	Quant method	Size
ColorShadow-7B-v2.Q2_K.gguf	Q2_K	2.53GB
ColorShadow-7B-v2.IQ3_XS.gguf	IQ3_XS	2.81GB
ColorShadow-7B-v2.IQ3_S.gguf	IQ3_S	2.96GB
ColorShadow-7B-v2.Q3_K_S.gguf	Q3_K_S	2.95GB
ColorShadow-7B-v2.IQ3_M.gguf	IQ3_M	3.06GB
ColorShadow-7B-v2.Q3_K.gguf	Q3_K	3.28GB
ColorShadow-7B-v2.Q3_K_M.gguf	Q3_K_M	3.28GB
ColorShadow-7B-v2.Q3_K_L.gguf	Q3_K_L	3.56GB
ColorShadow-7B-v2.IQ4_XS.gguf	IQ4_XS	3.67GB
ColorShadow-7B-v2.Q4_0.gguf	Q4_0	3.83GB
ColorShadow-7B-v2.IQ4_NL.gguf	IQ4_NL	3.87GB
ColorShadow-7B-v2.Q4_K_S.gguf	Q4_K_S	3.86GB
ColorShadow-7B-v2.Q4_K.gguf	Q4_K	4.07GB
ColorShadow-7B-v2.Q4_K_M.gguf	Q4_K_M	4.07GB
ColorShadow-7B-v2.Q4_1.gguf	Q4_1	4.24GB
ColorShadow-7B-v2.Q5_0.gguf	Q5_0	4.65GB
ColorShadow-7B-v2.Q5_K_S.gguf	Q5_K_S	4.65GB
ColorShadow-7B-v2.Q5_K.gguf	Q5_K	4.78GB
ColorShadow-7B-v2.Q5_K_M.gguf	Q5_K_M	4.78GB
ColorShadow-7B-v2.Q5_1.gguf	Q5_1	5.07GB
ColorShadow-7B-v2.Q6_K.gguf	Q6_K	5.53GB
ColorShadow-7B-v2.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 tags: - merge model-index: - name: ColorShadow-7B-v2 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: 67.15 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/ColorShadow-7B-v2 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: 84.69 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/ColorShadow-7B-v2 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: 60.34 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/ColorShadow-7B-v2 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: 62.93 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/ColorShadow-7B-v2 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=nlpguy/ColorShadow-7B-v2 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: 47.31 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/ColorShadow-7B-v2 name: Open LLM Leaderboard

ColorShadow-7B-v2

This is a Gradient-SLERP merge between diffnamehard/Mistral-CatMacaroni-slerp-7B and cookinai/Valkyrie-V1 performed using mergekit.

Here is the config file used:

  slices:
    - sources:
        - model: diffnamehard/Mistral-CatMacaroni-slerp-7B
          layer_range: [0, 32]
        - model: cookinai/Valkyrie-V1
          layer_range: [0, 32]
  merge_method: slerp
  base_model: diffnamehard/Mistral-CatMacaroni-slerp-7B
  parameters:
    t:
      - filter: self_attn
        value: [1, 0.5, 0.7, 0.3, 0]
      - filter: mlp
        value: [0, 0.5, 0.3, 0.7, 1]
      - value: 0.5 # fallback for rest of tensors
  dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	66.88
AI2 Reasoning Challenge (25-Shot)	67.15
HellaSwag (10-Shot)	84.69
MMLU (5-Shot)	60.34
TruthfulQA (0-shot)	62.93
Winogrande (5-shot)	78.85
GSM8k (5-shot)	47.31