README.md

metadata

language:
  - en
license: apache-2.0
datasets:
  - Open-Orca/SlimOrca
  - allenai/ultrafeedback_binarized_cleaned
model-index:
  - name: GALAXY_v03_slimorca_1_epoch_50k_DPO_1_epoch_30k
    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: 65.27
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY_v03_slimorca_1_epoch_50k_DPO_1_epoch_30k
          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: 85.62
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY_v03_slimorca_1_epoch_50k_DPO_1_epoch_30k
          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: 65.61
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY_v03_slimorca_1_epoch_50k_DPO_1_epoch_30k
          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: 53.46
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY_v03_slimorca_1_epoch_50k_DPO_1_epoch_30k
          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: 82.72
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY_v03_slimorca_1_epoch_50k_DPO_1_epoch_30k
          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: 0.08
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY_v03_slimorca_1_epoch_50k_DPO_1_epoch_30k
          name: Open LLM Leaderboard

TeeZee/GALAXY-XB-v1.03-SFT-DPO

Experiment, can DUS be taken one or more steps further?

Technical notes:

• model v03 finetuned on 50k entries from SlimOrca dataset and then DPO on 30k entries from ultrachat
• 12 layers removed from both models, 4 more than in original paper but its 1/4 of all layers(48) as per original paper.
• base version of upstage/SOLAR-10.7B-v1.0 used for merge

To evaluate

• model performance after DPO, did it recover all initial performance loss after merge?

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	58.79
AI2 Reasoning Challenge (25-Shot)	65.27
HellaSwag (10-Shot)	85.62
MMLU (5-Shot)	65.61
TruthfulQA (0-shot)	53.46
Winogrande (5-shot)	82.72
GSM8k (5-shot)	0.08