--- license: cc-by-4.0 tags: - merge - moe model-index: - name: Open_Gpt4_8x7B 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: 69.28 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rombodawg/Open_Gpt4_8x7B 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: 86.77 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rombodawg/Open_Gpt4_8x7B 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: 71.2 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rombodawg/Open_Gpt4_8x7B 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: 70.39 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rombodawg/Open_Gpt4_8x7B 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: 81.77 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rombodawg/Open_Gpt4_8x7B 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: 59.21 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rombodawg/Open_Gpt4_8x7B name: Open LLM Leaderboard --- Open_Gpt4 cc ____________________________________________________________________________________________ VERSION 0.2 OUT NOW: Fp16: - https://huggingface.co/rombodawg/Open_Gpt4_8x7B_v0.2 q8_0.gguf: - https://huggingface.co/rombodawg/Open_Gpt4_8x7B_v0.2_q8_0_gguf ____________________________________________________________________________________________ ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/T7QKB0fKNHQvNqAjm8zrH.jpeg) This model is a TIES merger of notux-8x7b-v1 and UNAversal-8x7B-v1beta with MixtralOrochi8x7B being the Base model. I was very impressed with MixtralOrochi8x7B performance and multifaceted usecases as it is already a merger of many usefull Mixtral models such as Mixtral instruct, Noromaid-v0.1-mixtral, openbuddy-mixtral and possibly other models that were not named. My goal was to expand the models capabilities and make it even more useful of a model, maybe even competitive with closed source models like Gpt-4. But for that more testing is required. I hope the community can help me determine if its deserving of its name. 😊 Base model: - https://huggingface.co/smelborp/MixtralOrochi8x7B Merged models: - https://huggingface.co/fblgit/UNAversal-8x7B-v1beta - https://huggingface.co/argilla/notux-8x7b-v1 Instruct template: Alpaca Merger config: ```yaml models: - model: notux-8x7b-v1 parameters: density: .5 weight: 1 - model: UNAversal-8x7B-v1beta parameters: density: .5 weight: 1 merge_method: ties base_model: MixtralOrochi8x7B parameters: normalize: true int8_mask: true dtype: float16 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_rombodawg__Open_Gpt4_8x7B) | Metric |Value| |---------------------------------|----:| |Avg. |73.10| |AI2 Reasoning Challenge (25-Shot)|69.28| |HellaSwag (10-Shot) |86.77| |MMLU (5-Shot) |71.20| |TruthfulQA (0-shot) |70.39| |Winogrande (5-shot) |81.77| |GSM8k (5-shot) |59.21|