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--- |
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library_name: transformers |
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tags: |
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- merge |
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- llama |
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- dpo |
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base_model: |
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- akjindal53244/Llama-3.1-Storm-8B |
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- Sao10K/L3.1-8B-Niitama-v1.1 |
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- v000000/L3.1-Niitorm-8B-t0.0001 |
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datasets: |
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- jondurbin/gutenberg-dpo-v0.1 |
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model-index: |
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- name: L3.1-Niitorm-8B-DPO-t0.0001 |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: IFEval (0-Shot) |
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type: HuggingFaceH4/ifeval |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: inst_level_strict_acc and prompt_level_strict_acc |
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value: 76.89 |
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name: strict accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=v000000/L3.1-Niitorm-8B-DPO-t0.0001 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: BBH (3-Shot) |
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type: BBH |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc_norm |
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value: 30.51 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=v000000/L3.1-Niitorm-8B-DPO-t0.0001 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MATH Lvl 5 (4-Shot) |
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type: hendrycks/competition_math |
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args: |
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num_few_shot: 4 |
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metrics: |
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- type: exact_match |
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value: 14.88 |
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name: exact match |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=v000000/L3.1-Niitorm-8B-DPO-t0.0001 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GPQA (0-shot) |
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type: Idavidrein/gpqa |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 5.93 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=v000000/L3.1-Niitorm-8B-DPO-t0.0001 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MuSR (0-shot) |
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type: TAUR-Lab/MuSR |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 7.26 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=v000000/L3.1-Niitorm-8B-DPO-t0.0001 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU-PRO (5-shot) |
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type: TIGER-Lab/MMLU-Pro |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 31.85 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=v000000/L3.1-Niitorm-8B-DPO-t0.0001 |
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name: Open LLM Leaderboard |
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--- |
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# Llama-3.1-Niitorm-8B-DPO |
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* *DPO Trained, Llama3.1-8B.* |
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/64f74b6e6389380c77562762/QeNjtwolNpxUmpo9NL7VI.png) |
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<b>New: DPO'd Gutenberg Version (full epoch training).</b> |
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RP model, Niitama 1.1 as a base, nearswapped with one of the smartest 3.1 models "Storm", then DPO'd, mostly abliterated. |
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Essentially, it's an improved Niitama 1.1 |
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------------------------------------------------------------------------------- |
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*Gutenberg DPO creates more human-like prose/story writing and greately lessen synthetic feeling outputs.* |
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------------------------------------------------------------------------------- |
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# *llama.cpp:* |
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# thank you, mradermacher (GGUF) |
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* [GGUF static](https://huggingface.co/mradermacher/L3.1-Niitorm-8B-DPO-t0.0001-GGUF) |
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* [GGUF Imatrix](https://huggingface.co/mradermacher/L3.1-Niitorm-8B-DPO-t0.0001-i1-GGUF) |
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# thank you, QuantFactory (GGUF) |
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* [GGUF static](https://huggingface.co/QuantFactory/L3.1-Niitorm-8B-DPO-t0.0001-GGUF) |
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# v0 (GGUF) |
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* [GGUF Imatrix](https://huggingface.co/v000000/L3.1-Niitorm-8B-DPO-t0.0001-GGUFs-IMATRIX) *-only q8, q6 k, q5 k s, q4 k s, iq4 x s* |
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## Finetune and merge |
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This is a merge and finetune of pre-trained language models. |
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*Resultant merge finetuned* on [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1) for 1 epoch, 1.5e-5 learning rate, on Nvidia A100. |
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## Merge Details |
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### Merge Method |
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This model was merged using the <b>NEARSWAP t0.0001</b> merge algorithm. |
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### Models Merged |
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The following models were included in the merge: |
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* Base Model: [Sao10K/L3.1-8B-Niitama-v1.1](https://huggingface.co/Sao10K/L3.1-8B-Niitama-v1.1) + [grimjim/Llama-3-Instruct-abliteration-LoRA-8B](https://huggingface.co/grimjim/Llama-3-Instruct-abliteration-LoRA-8B) |
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* [akjindal53244/Llama-3.1-Storm-8B](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B) |
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### Configuration |
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The following YAML configuration was used to produce this model: |
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```yaml |
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slices: |
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- sources: |
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- model: Sao10K/L3.1-8B-Niitama-v1.1+grimjim/Llama-3-Instruct-abliteration-LoRA-8B |
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layer_range: [0, 32] |
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- model: akjindal53244/Llama-3.1-Storm-8B |
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layer_range: [0, 32] |
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merge_method: nearswap |
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base_model: Sao10K/L3.1-8B-Niitama-v1.1+grimjim/Llama-3-Instruct-abliteration-LoRA-8B |
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parameters: |
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t: |
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- value: 0.0001 |
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dtype: float16 |
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# Then, DPO Finetune |
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# [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1) |
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``` |
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### DPO Notes |
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*I used a higher learning rate and full dataset when training compared to my "L3.1-Celestial-Stone-2x8B-DPO". This caused lower loss and better adaption to the chosen style.* |
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------------------------------------------------------------------------------- |
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# Prompt Template: |
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```bash |
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<|begin_of_text|><|start_header_id|>system<|end_header_id|> |
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{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> |
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{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|> |
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{output}<|eot_id|> |
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``` |
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Credit to Alchemonaut. |
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Credit to Sao10K. |
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Credit to Grimjim. |
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Credit to mlabonne. |
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Credit to jondurbin. |
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Credit to woofwolfy. |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_v000000__L3.1-Niitorm-8B-DPO-t0.0001) |
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| Metric |Value| |
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|-------------------|----:| |
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|Avg. |27.89| |
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|IFEval (0-Shot) |76.89| |
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|BBH (3-Shot) |30.51| |
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|MATH Lvl 5 (4-Shot)|14.88| |
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|GPQA (0-shot) | 5.93| |
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|MuSR (0-shot) | 7.26| |
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|MMLU-PRO (5-shot) |31.85| |
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