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README.md
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Quantization made by Richard Erkhov.
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[Github](https://github.com/RichardErkhov)
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[Discord](https://discord.gg/pvy7H8DZMG)
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[Request more models](https://github.com/RichardErkhov/quant_request)
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OpenDolphinHermes_Llama2_7B - GGUF
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- Model creator: https://huggingface.co/sethuiyer/
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- Original model: https://huggingface.co/sethuiyer/OpenDolphinHermes_Llama2_7B/
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [OpenDolphinHermes_Llama2_7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q2_K.gguf) | Q2_K | 2.36GB |
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| [OpenDolphinHermes_Llama2_7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.IQ3_XS.gguf) | IQ3_XS | 2.6GB |
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| [OpenDolphinHermes_Llama2_7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.IQ3_S.gguf) | IQ3_S | 2.75GB |
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| [OpenDolphinHermes_Llama2_7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q3_K_S.gguf) | Q3_K_S | 2.75GB |
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| [OpenDolphinHermes_Llama2_7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.IQ3_M.gguf) | IQ3_M | 2.9GB |
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| [OpenDolphinHermes_Llama2_7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q3_K.gguf) | Q3_K | 3.07GB |
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| [OpenDolphinHermes_Llama2_7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q3_K_M.gguf) | Q3_K_M | 3.07GB |
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| [OpenDolphinHermes_Llama2_7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q3_K_L.gguf) | Q3_K_L | 3.35GB |
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| [OpenDolphinHermes_Llama2_7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.IQ4_XS.gguf) | IQ4_XS | 3.4GB |
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| [OpenDolphinHermes_Llama2_7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q4_0.gguf) | Q4_0 | 3.56GB |
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| [OpenDolphinHermes_Llama2_7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.IQ4_NL.gguf) | IQ4_NL | 3.58GB |
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| [OpenDolphinHermes_Llama2_7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q4_K_S.gguf) | Q4_K_S | 3.59GB |
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| [OpenDolphinHermes_Llama2_7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q4_K.gguf) | Q4_K | 3.8GB |
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| [OpenDolphinHermes_Llama2_7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q4_K_M.gguf) | Q4_K_M | 3.8GB |
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| [OpenDolphinHermes_Llama2_7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q4_1.gguf) | Q4_1 | 3.95GB |
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| [OpenDolphinHermes_Llama2_7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q5_0.gguf) | Q5_0 | 4.33GB |
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| [OpenDolphinHermes_Llama2_7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q5_K_S.gguf) | Q5_K_S | 4.33GB |
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| [OpenDolphinHermes_Llama2_7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q5_K.gguf) | Q5_K | 4.45GB |
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| [OpenDolphinHermes_Llama2_7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q5_K_M.gguf) | Q5_K_M | 4.45GB |
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| [OpenDolphinHermes_Llama2_7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q5_1.gguf) | Q5_1 | 4.72GB |
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| [OpenDolphinHermes_Llama2_7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q6_K.gguf) | Q6_K | 5.15GB |
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| [OpenDolphinHermes_Llama2_7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf/blob/main/OpenDolphinHermes_Llama2_7B.Q8_0.gguf) | Q8_0 | 6.67GB |
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Original model description:
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---
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language:
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- en
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license: llama2
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library_name: transformers
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tags:
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- merge
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- mergekit
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- lazymergekit
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datasets:
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- teknium/openhermes
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- cognitivecomputations/dolphin
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base_model:
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- cognitivecomputations/dolphin-llama2-7b
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- Tensoic/Llama-2-openhermes
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pipeline_tag: text-generation
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model-index:
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- name: OpenDolphinHermes_Llama2_7B
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 55.03
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 78.74
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 52.25
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 46.1
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 73.16
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 20.17
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B
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name: Open LLM Leaderboard
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---
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# OpenDolphinHermes_Llama2_7B
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<p align="center">
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<img src="https://huggingface.co/sethuiyer/OpenDolphinHermes_Llama2_7B/resolve/main/dolphin_hermes.webp" height="256px" alt="SynthIQ">
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</p>
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mergekit SLERP of these two models
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* [cognitivecomputations/dolphin-llama2-7b](https://huggingface.co/cognitivecomputations/dolphin-llama2-7b)
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* [Tensoic/Llama-2-openhermes](https://huggingface.co/Tensoic/Llama-2-openhermes)
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## 🧩 Configuration
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```yaml
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slices:
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- sources:
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- model: cognitivecomputations/dolphin-llama2-7b
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layer_range: [0, 32]
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- model: Tensoic/Llama-2-openhermes
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layer_range: [0, 32]
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merge_method: slerp
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base_model: Tensoic/Llama-2-openhermes
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parameters:
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t:
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- filter: self_attn
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value: [0, 0.5, 0.3, 0.7, 1]
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- filter: mlp
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value: [1, 0.5, 0.7, 0.3, 0]
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- value: 0.5
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dtype: bfloat16
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```
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# Prompt Template (ChatML)
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```text
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<|im_start|>system
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You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.
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Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.
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Please ensure that your responses are socially unbiased and positive in nature.
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If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct.
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If you don't know the answer to a question, please don't share false information.
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<|im_end|>
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<|im_start|>user
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{ .Prompt}
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<|im_end|>
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<|im_start|>assistant
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```
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# OpenLLM Leaderboard
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| T | Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
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|---|--------------------------------------------|---------|------|-----------|-------|------------|------------|-------|
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| 0 | meta-llama/llama-2-13b-hf | 55.69 | 59.39 | 82.13 | 55.77 | 37.38 | 76.64 | 22.82 |
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+
| 1 | sethuiyer/OpenDolphinHermes_Llama2_7B | 54.24 | 55.03| 78.74 | 52.25 | 46.1 | 73.16 | 20.17 |
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+
| 2 | togethercomputer/Llama-2-7B-32K-Instruct | 50.02 | 51.11| 78.51 | 46.11 | 44.86 | 73.88 | 5.69 |
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+
| 3 | togethercomputer/LLaMa-2-7B-32K | 47.07 | 47.53| 76.14 | 43.33 | 39.23 | 71.9 | 4.32 |
|
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+
|
222 |
+
## Why?
|
223 |
+
|
224 |
+
I wanted a LLaMa2-7B model which is as good as base LLaMa2-13B model.
|
225 |
+
|
226 |
+
## 💻 Usage
|
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+
|
228 |
+
```python
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229 |
+
!pip install -qU transformers accelerate
|
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+
|
231 |
+
from transformers import AutoTokenizer
|
232 |
+
import transformers
|
233 |
+
import torch
|
234 |
+
|
235 |
+
model = "sethuiyer/OpenDolphinHermes_Llama2_7B"
|
236 |
+
messages = [{"role": "user", "content": "What is a large language model?"}]
|
237 |
+
|
238 |
+
tokenizer = AutoTokenizer.from_pretrained(model)
|
239 |
+
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
240 |
+
pipeline = transformers.pipeline(
|
241 |
+
"text-generation",
|
242 |
+
model=model,
|
243 |
+
torch_dtype=torch.float16,
|
244 |
+
device_map="auto",
|
245 |
+
)
|
246 |
+
|
247 |
+
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
|
248 |
+
print(outputs[0]["generated_text"])
|
249 |
+
```
|
250 |
+
|
251 |
+
Output:
|
252 |
+
```text
|
253 |
+
A large language model is a type of artificial intelligence system that has been trained on a massive amount of data, often millions or even billions of words, to learn the patterns and relationships between words and phrases.
|
254 |
+
These models can then be used to generate new text, understand and translate languages, and perform various natural language processing tasks.
|
255 |
+
They have become increasingly popular in recent years due to advances in machine learning technology and their ability to achieve high levels of accuracy and performance on natural language processing tasks.
|
256 |
+
Examples of large language models include GPT-2, BERT, and T5.
|
257 |
+
```
|
258 |
+
## Thanks
|
259 |
+
Thanks to Google Colab for the compute.
|
260 |
+
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
|
261 |
+
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_sethuiyer__OpenDolphinHermes_Llama2_7B)
|
262 |
+
|
263 |
+
| Metric |Value|
|
264 |
+
|---------------------------------|----:|
|
265 |
+
|Avg. |54.24|
|
266 |
+
|AI2 Reasoning Challenge (25-Shot)|55.03|
|
267 |
+
|HellaSwag (10-Shot) |78.74|
|
268 |
+
|MMLU (5-Shot) |52.25|
|
269 |
+
|TruthfulQA (0-shot) |46.10|
|
270 |
+
|Winogrande (5-shot) |73.16|
|
271 |
+
|GSM8k (5-shot) |20.17|
|
272 |
+
|
273 |
+
|
274 |
+
|