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