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RichardErkhov/
MaziyarPanahi_-_TheTop-5x7B-Instruct-T-v0.1-gguf

Name: MaziyarPanahi_-_TheTop-5x7B-Instruct-T-v0.1-gguf
Availability: InStock

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gguf

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Quantization made by Richard Erkhov.

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TheTop-5x7B-Instruct-T-v0.1 - GGUF

Model creator: https://huggingface.co/MaziyarPanahi/
Original model: https://huggingface.co/MaziyarPanahi/TheTop-5x7B-Instruct-T-v0.1/

Name	Quant method	Size
TheTop-5x7B-Instruct-T-v0.1.Q2_K.gguf	Q2_K	2.53GB
TheTop-5x7B-Instruct-T-v0.1.IQ3_XS.gguf	IQ3_XS	2.81GB
TheTop-5x7B-Instruct-T-v0.1.IQ3_S.gguf	IQ3_S	2.96GB
TheTop-5x7B-Instruct-T-v0.1.Q3_K_S.gguf	Q3_K_S	2.95GB
TheTop-5x7B-Instruct-T-v0.1.IQ3_M.gguf	IQ3_M	3.06GB
TheTop-5x7B-Instruct-T-v0.1.Q3_K.gguf	Q3_K	3.28GB
TheTop-5x7B-Instruct-T-v0.1.Q3_K_M.gguf	Q3_K_M	3.28GB
TheTop-5x7B-Instruct-T-v0.1.Q3_K_L.gguf	Q3_K_L	3.56GB
TheTop-5x7B-Instruct-T-v0.1.IQ4_XS.gguf	IQ4_XS	3.67GB
TheTop-5x7B-Instruct-T-v0.1.Q4_0.gguf	Q4_0	3.83GB
TheTop-5x7B-Instruct-T-v0.1.IQ4_NL.gguf	IQ4_NL	3.87GB
TheTop-5x7B-Instruct-T-v0.1.Q4_K_S.gguf	Q4_K_S	3.86GB
TheTop-5x7B-Instruct-T-v0.1.Q4_K.gguf	Q4_K	4.07GB
TheTop-5x7B-Instruct-T-v0.1.Q4_K_M.gguf	Q4_K_M	4.07GB
TheTop-5x7B-Instruct-T-v0.1.Q4_1.gguf	Q4_1	4.24GB
TheTop-5x7B-Instruct-T-v0.1.Q5_0.gguf	Q5_0	4.65GB
TheTop-5x7B-Instruct-T-v0.1.Q5_K_S.gguf	Q5_K_S	4.65GB
TheTop-5x7B-Instruct-T-v0.1.Q5_K.gguf	Q5_K	4.78GB
TheTop-5x7B-Instruct-T-v0.1.Q5_K_M.gguf	Q5_K_M	4.78GB
TheTop-5x7B-Instruct-T-v0.1.Q5_1.gguf	Q5_1	5.07GB
TheTop-5x7B-Instruct-T-v0.1.Q6_K.gguf	Q6_K	5.53GB
TheTop-5x7B-Instruct-T-v0.1.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 library_name: transformers tags:

merge pipeline_tag: text-generation model-index:
name: TheTop-5x7B-Instruct-T-v0.1 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: 73.63 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/TheTop-5x7B-Instruct-T-v0.1 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: 88.85 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/TheTop-5x7B-Instruct-T-v0.1 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: 64.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/TheTop-5x7B-Instruct-T-v0.1 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: 70.78 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/TheTop-5x7B-Instruct-T-v0.1 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: 85.79 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/TheTop-5x7B-Instruct-T-v0.1 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: 66.49 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/TheTop-5x7B-Instruct-T-v0.1 name: Open LLM Leaderboard

Merge of top 7B models with TIES method

mergekit is a toolkit for merging pre-trained language models. mergekit uses an out-of-core approach to perform unreasonably elaborate merges in resource-constrained situations. Merges can be run entirely on CPU or accelerated with as little as 8 GB of VRAM. Many merging algorithms are supported, with more coming as they catch my attention.

Eval

python

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}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	74.96
AI2 Reasoning Challenge (25-Shot)	73.63
HellaSwag (10-Shot)	88.85
MMLU (5-Shot)	64.22
TruthfulQA (0-shot)	70.78
Winogrande (5-shot)	85.79
GSM8k (5-shot)	66.49

RichardErkhov/MaziyarPanahi_-_TheTop-5x7B-Instruct-T-v0.1-gguf

Original model description:

Eval

Open LLM Leaderboard Evaluation Results

RichardErkhov/
MaziyarPanahi_-_TheTop-5x7B-Instruct-T-v0.1-gguf