license, model-index

license

model-index

apache-2.0

name results GALAXY-XB-v.03 task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 61.77 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.03 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 83.59 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.03 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 64.55 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.03 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 44.19 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.03 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 81.06 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.03 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 45.03 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.03 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

Detailed results can be found here

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