titan087's picture
Update README.md
1f14864 verified
metadata
language:
  - en
license: apache-2.0
library_name: transformers
model-index:
  - name: Rhea-72b-v0.5
    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: 79.78
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5
          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: 91.15
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5
          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: 77.95
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5
          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: 74.5
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5
          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: 87.85
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5
          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: 76.12
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5
          name: Open LLM Leaderboard

exllama2 quantization - 4bpw

Rhea-72b-v0.5

image/jpeg

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 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.

Our model ranked first on HuggingFace's Open LLM leaderboard.

SGD : A Study on Self-Generated Dataset creation method for DPO Learning

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.

Model Details

  • Model Developers : davidkim(changyeon kim)
  • Repository : https://github.com/davidkim205/nox
  • base mode : abacusai/Smaug-72B-v0.1
  • sft dataset : datasets_enconv_4m
  • dpo dataset : datasets_encomp_151k

sft dataset info : datasets_enconv_4m

100k random shuffle datasets

  • stack-exchange-preferences
  • SlimOrca
  • alpaca-gpt4
  • SHP
  • HC3
  • databricks-dolly-15k
  • orca-dpo-pairs
  • us-stockname
  • OpenHermes2.5-dpo-binarized-alpha
  • distilabel-math-preference-dpo
  • Neural-DPO
  • truthy-dpo-v0.1
  • distilabel-capybara-dpo-7k-binarized
  • us-sentiment
  • contextual-dpo-v0.1

1k random shuffle datasets

  • bigbench
  • glue_mnli
  • glue_qqp
  • xnli
  • codexglue_code2text_go
  • trivia_qa
  • medmcqa
  • hendrycks_ethics
  • super_glue_record
  • glue_qnli
  • anli_r3
  • swag
  • squad_v2
  • nq_open
  • drop
  • glue_sst2
  • blimp
  • paws-x
  • unscramble
  • anli_r2
  • babi
  • math_qa
  • social_i_qa
  • piqa
  • arithmetic
  • anli_r1
  • prost
  • sciq
  • mc_taco
  • medqa
  • super_glue_boolq
  • hendrycks_math
  • lambada
  • toxigen-data
  • glue_cola
  • pubmed_qa
  • logiqa
  • mutual
  • headqa
  • bbh
  • super_glue_wic
  • openbookqa
  • glue_mrpc
  • web_questions
  • qasper
  • super_glue_multirc
  • story_cloze
  • super_glue_rte
  • glue_rte
  • race
  • xwinograd
  • asdiv
  • xstory_cloze
  • crows_pairs_multilingual
  • belebele
  • glue_wnli
  • super_glue_wsc
  • coqa
  • super_glue_copa
  • super_glue_cb
  • winograd_wsc
  • mgsm
  • scrolls_contract_nli
  • If the data set cannot be found, it is internal company data and cannot be made public.

dpo dataset info : datasets_encomp_151k

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.

  • I'm sorry I can't reveal it.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 81.22
AI2 Reasoning Challenge (25-Shot) 79.78
HellaSwag (10-Shot) 91.15
MMLU (5-Shot) 77.95
TruthfulQA (0-shot) 74.50
Winogrande (5-shot) 87.85
GSM8k (5-shot) 76.12