GALAXY-XB-v.03/README.md

---
license: apache-2.0
model-index:
- name: GALAXY-XB-v.03
  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: 61.77
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.03
      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: 83.59
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.03
      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: 64.55
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.03
      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: 44.19
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.03
      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: 81.06
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.03
      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: 45.03
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.03
      name: Open LLM Leaderboard
---
### TeeZee/GALAXY-XB-v.03 ###

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

### Technical notes:
- 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
- no finetuning done yet, this is just a merge, first step in DUS paper
- next step, if evaluation proves that its at least as 'smart' as base model, should be finetuning to 'recover' after merge
# [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_TeeZee__GALAXY-XB-v.03)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |63.37|
|AI2 Reasoning Challenge (25-Shot)|61.77|
|HellaSwag (10-Shot)              |83.59|
|MMLU (5-Shot)                    |64.55|
|TruthfulQA (0-shot)              |44.19|
|Winogrande (5-shot)              |81.06|
|GSM8k (5-shot)                   |45.03|

### Results
- small quality loss can be observed comparing to base model, as described in the DUS paper
- this merge has best evaluation results, so it will be finetuned to 'recover' from the merge
- finetunig will be done on 5-10% of openorca dataset and full DPO datasets used by SOLAR
- v03 > v01 > v02 - based on average evaluation scores, removing 1/4 of total layers seems to be the correct way to scale DUS