license, tags, base_model, datasets, model-index

license	tags	base_model	datasets	model-index
apache-2.0	UNA simple-math juanako	abacusai/Smaug-34B-v0.1	fblgit/simple-math jondurbin/bagel-v0.3	name results UNA-SimpleSmaug-34b-v1beta 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 74.57 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta 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 86.74 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta 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 76.68 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta 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 70.17 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta 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 83.82 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta 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 72.48 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type args IFEval (0-Shot) HuggingFaceH4/ifeval num_few_shot 0 type value name inst_level_strict_acc and prompt_level_strict_acc 45.56 strict accuracy url name https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type args BBH (3-Shot) BBH num_few_shot 3 type value name acc_norm 32.78 normalized accuracy url name https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type args MATH Lvl 5 (4-Shot) hendrycks/competition_math num_few_shot 4 type value name exact_match 0.15 exact match url name https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type args GPQA (0-shot) Idavidrein/gpqa num_few_shot 0 type value name acc_norm 8.95 acc_norm url name https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type args MuSR (0-shot) TAUR-Lab/MuSR num_few_shot 0 type value name acc_norm 11.96 acc_norm url name https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU-PRO (5-shot) TIGER-Lab/MMLU-Pro main test num_few_shot 5 type value name acc 39.33 accuracy url name https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta Open LLM Leaderboard

UNA-SimpleSmaug-34b-v1beta

Scoring 04-February-2024 #1 34B model, outperforming its original base model Smaug-34B-v0.1 with 77.41 😎 Oh, btw.. this one went thru SFT so the abacus inside Smaug is back to normal.. so you can further train/dpo him .. RESET!..

UPDATES March : Stills undisputed 34B King Smaug 70B stills undisputed 70B King

==== And people wonders.. why there is no UNA of Hermes or Smaug 70B? << i dont think is worth the time to spend on a model that is widely known for not being too useful, likely UNA can fix some of the internal mess.. for Hermes, we spoke chitchat quick a couple times but nothing solid, but we would like to make a reborn of excellent models using UNA, just liek we did with UNA-Dolphin where we saw relevant performance is short time.

Applied UNA only on the Attention, not on the MLP's

• Is based on Smaug
• SimpleMath dataset
• It was trained on Axolotl

Experiment

The thing here is to understand whats the impact of SimpleMath applied at the attention layer during a SFT session and how it impacts on the neural network overall.

Results: Improving mathematican and reasoning capabilities without degrading and presserving previous training sessions.

And enjoy our ModelSimilarities tool detector https://github.com/fblgit/model-similarity where we confirmed numerically the bloodties of the model.

Evals

Metric	Value
Avg.	77.41
AI2 Reasoning Challenge (25-Shot)	74.57
HellaSwag (10-Shot)	86.74
MMLU (5-Shot)	76.68
TruthfulQA (0-shot)	70.17
Winogrande (5-shot)	83.82
GSM8k (5-shot)	72.48

|    Task     |Version| Metric |Value            |
|-------------|------:|--------|----------------:|
|arc_challenge|     HF|acc_norm| 0.7457337883959 |
|gsm8k        |     HF|acc     | 0.7247915087187 |
|mmlu         |     HF|acc     | 0.7649553475572 |
|mmlu         |     HF|acc_norm| 0.7681713551647 |
|hellaswag    |     HF|acc_norm| 0.8673571001792 | 
|truthfulqa   |     HF|mc2     | 0.7016557407771 |
|winogrande   |     HF|acc     | 0.8382004735595 |
|------------------------------------------------|

Increasing GSM, MMLU, ARC, WINO.

Citations

To abacusai for making Smaug-34B, the Bagel, and all the magic behind the base model.

If you use the model, provide citation even for merges or anything.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	23.12
IFEval (0-Shot)	45.56
BBH (3-Shot)	32.78
MATH Lvl 5 (4-Shot)	0.15
GPQA (0-shot)	8.95
MuSR (0-shot)	11.96
MMLU-PRO (5-shot)	39.33