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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: transformers |
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model-index: |
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- name: Rhea-72b-v0.5 |
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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: 79.78 |
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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=davidkim205/Rhea-72b-v0.5 |
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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: 91.15 |
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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=davidkim205/Rhea-72b-v0.5 |
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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: 77.95 |
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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=davidkim205/Rhea-72b-v0.5 |
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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: 74.5 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5 |
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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: 87.85 |
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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=davidkim205/Rhea-72b-v0.5 |
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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: 76.12 |
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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=davidkim205/Rhea-72b-v0.5 |
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name: Open LLM Leaderboard |
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--- |
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exllama2 quantization - 4bpw |
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# Rhea-72b-v0.5 |
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The Rhea project is a project that conducts research on various learning methods to improve llm model performance. We fine-tuned the existing model using the [nox](https://github.com/davidkim205/nox) framework. We built a dataset for SFT learning based on the currently open dataset, and created a dataset using SGD (Self-Generated Dataset Creation Method for DPO Learning) for DPO learning. |
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Our model ranked first on HuggingFace's Open LLM leaderboard. |
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## SGD : A Study on Self-Generated Dataset creation method for DPO Learning |
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This method proposes a novel method for generating datasets for DPO (Self-supervised Learning) models. We suggest a technique where sentences generated by the model are compared with the actual correct answers from an existing dataset, and sentences where the model's generated results do not match the correct answers are added. This enables the model to autonomously create training data, thereby enhancing the performance of DPO models. |
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## Model Details |
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* **Model Developers** : davidkim(changyeon kim) |
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* **Repository** : [https://github.com/davidkim205/nox](https://github.com/davidkim205/nox) |
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* **base mode** : abacusai/Smaug-72B-v0.1 |
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* **sft dataset** : datasets_enconv_4m |
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* **dpo dataset** : datasets_encomp_151k |
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## sft dataset info : datasets_enconv_4m |
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### 100k random shuffle datasets |
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- stack-exchange-preferences |
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- SlimOrca |
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- alpaca-gpt4 |
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- SHP |
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- HC3 |
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- databricks-dolly-15k |
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- orca-dpo-pairs |
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- us-stockname |
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- OpenHermes2.5-dpo-binarized-alpha |
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- distilabel-math-preference-dpo |
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- Neural-DPO |
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- truthy-dpo-v0.1 |
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- distilabel-capybara-dpo-7k-binarized |
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- us-sentiment |
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- contextual-dpo-v0.1 |
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### 1k random shuffle datasets |
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- bigbench |
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- glue_mnli |
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- glue_qqp |
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- xnli |
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- codexglue_code2text_go |
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- trivia_qa |
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- medmcqa |
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- hendrycks_ethics |
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- super_glue_record |
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- glue_qnli |
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- anli_r3 |
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- swag |
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- squad_v2 |
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- nq_open |
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- drop |
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- glue_sst2 |
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- blimp |
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- paws-x |
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- unscramble |
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- anli_r2 |
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- babi |
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- math_qa |
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- social_i_qa |
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- piqa |
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- arithmetic |
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- anli_r1 |
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- prost |
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- sciq |
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- mc_taco |
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- medqa |
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- super_glue_boolq |
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- hendrycks_math |
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- lambada |
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- toxigen-data |
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- glue_cola |
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- pubmed_qa |
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- logiqa |
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- mutual |
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- headqa |
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- bbh |
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- super_glue_wic |
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- openbookqa |
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- glue_mrpc |
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- web_questions |
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- qasper |
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- super_glue_multirc |
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- story_cloze |
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- super_glue_rte |
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- glue_rte |
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- race |
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- xwinograd |
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- asdiv |
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- xstory_cloze |
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- crows_pairs_multilingual |
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- belebele |
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- glue_wnli |
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- super_glue_wsc |
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- coqa |
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- super_glue_copa |
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- super_glue_cb |
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- winograd_wsc |
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- mgsm |
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- scrolls_contract_nli |
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* If the data set cannot be found, it is internal company data and cannot be made public. |
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## dpo dataset info : datasets_encomp_151k |
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Randomly selecting data from each category within the training dataset, we constructed a DPO (Direct Preference Optimization) dataset using sentences with logits lower than the mean within the model-generated sentences. |
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* I'm sorry I can't reveal it. |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_davidkim205__Rhea-72b-v0.5) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |81.22| |
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|AI2 Reasoning Challenge (25-Shot)|79.78| |
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|HellaSwag (10-Shot) |91.15| |
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|MMLU (5-Shot) |77.95| |
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|TruthfulQA (0-shot) |74.50| |
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|Winogrande (5-shot) |87.85| |
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|GSM8k (5-shot) |76.12| |
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