RichardErkhov/ChaoticNeutrals_-_Prodigy_7B-gguf

Quantization made by Richard Erkhov.

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Prodigy_7B - GGUF

• Model creator: https://huggingface.co/ChaoticNeutrals/
• Original model: https://huggingface.co/ChaoticNeutrals/Prodigy_7B/

Name	Quant method	Size
Prodigy_7B.Q2_K.gguf	Q2_K	2.53GB
Prodigy_7B.IQ3_XS.gguf	IQ3_XS	2.81GB
Prodigy_7B.IQ3_S.gguf	IQ3_S	2.96GB
Prodigy_7B.Q3_K_S.gguf	Q3_K_S	2.95GB
Prodigy_7B.IQ3_M.gguf	IQ3_M	3.06GB
Prodigy_7B.Q3_K.gguf	Q3_K	3.28GB
Prodigy_7B.Q3_K_M.gguf	Q3_K_M	3.28GB
Prodigy_7B.Q3_K_L.gguf	Q3_K_L	3.56GB
Prodigy_7B.IQ4_XS.gguf	IQ4_XS	3.67GB
Prodigy_7B.Q4_0.gguf	Q4_0	3.83GB
Prodigy_7B.IQ4_NL.gguf	IQ4_NL	3.87GB
Prodigy_7B.Q4_K_S.gguf	Q4_K_S	3.86GB
Prodigy_7B.Q4_K.gguf	Q4_K	4.07GB
Prodigy_7B.Q4_K_M.gguf	Q4_K_M	4.07GB
Prodigy_7B.Q4_1.gguf	Q4_1	4.24GB
Prodigy_7B.Q5_0.gguf	Q5_0	4.65GB
Prodigy_7B.Q5_K_S.gguf	Q5_K_S	4.65GB
Prodigy_7B.Q5_K.gguf	Q5_K	4.78GB
Prodigy_7B.Q5_K_M.gguf	Q5_K_M	4.78GB
Prodigy_7B.Q5_1.gguf	Q5_1	5.07GB
Prodigy_7B.Q6_K.gguf	Q6_K	5.53GB
Prodigy_7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: other library_name: transformers tags: - mergekit - merge base_model: - macadeliccc/WestLake-7B-v2-laser-truthy-dpo - ChaoticNeutrals/This_is_fine_7B model-index: - name: Prodigy_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: 71.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prodigy_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: 88.09 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prodigy_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: 64.92 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prodigy_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: 68.57 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prodigy_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: 84.53 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prodigy_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: 64.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prodigy_7B name: Open LLM Leaderboard

Wing

GGUF available here: https://huggingface.co/Lewdiculous/Prodigy_7B-GGUF-Imatrix

Big thanks to https://huggingface.co/Lewdiculous

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:

• macadeliccc/WestLake-7B-v2-laser-truthy-dpo
• ChaoticNeutrals/This_is_fine_7B

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: ChaoticNeutrals/This_is_fine_7B
        layer_range: [0, 32]
      - model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
        layer_range: [0, 32]
merge_method: slerp
base_model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
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.	73.68
AI2 Reasoning Challenge (25-Shot)	71.59
HellaSwag (10-Shot)	88.09
MMLU (5-Shot)	64.92
TruthfulQA (0-shot)	68.57
Winogrande (5-shot)	84.53
GSM8k (5-shot)	64.37