初始化项目，由ModelHub XC社区提供模型

Model: TeeZee/GALAXY-XB-v.03
Source: Original Platform

README.md

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+---
+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
+
+