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--- |
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license: mit |
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model-index: |
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- name: Medium |
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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: 44.06 |
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name: strict accuracy |
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- type: inst_level_strict_acc and prompt_level_strict_acc |
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value: 44.06 |
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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=dnhkng/Medium |
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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: 47.73 |
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name: normalized accuracy |
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- type: acc_norm |
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value: 47.73 |
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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=dnhkng/Medium |
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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: 7.78 |
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name: exact match |
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- type: exact_match |
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value: 7.78 |
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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=dnhkng/Medium |
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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: 10.4 |
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name: acc_norm |
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- type: acc_norm |
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value: 10.4 |
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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=dnhkng/Medium |
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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: 8.73 |
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name: acc_norm |
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- type: acc_norm |
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value: 8.73 |
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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=dnhkng/Medium |
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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: 36.96 |
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name: accuracy |
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- type: acc |
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value: 36.96 |
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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=dnhkng/Medium |
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name: Open LLM Leaderboard |
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--- |
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This is a new kind of model optimization. A paper on the technique is currently being written. |
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This research was supported with hardware from the [appliedAI Institute](https://www.appliedai-institute.de/en/), whose goal is to generate and communicate high-quality knowledge about trustworthy AI. |
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## Quickstart |
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This code snippets show how to get quickly started with running the model on a GPU: |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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torch.random.manual_seed(0) |
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model_id = "dnhkng/Medium" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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device_map="cuda", |
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torch_dtype="auto", |
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trust_remote_code=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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messages = [ |
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{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, |
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{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, |
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{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, |
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] |
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pipe = pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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) |
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generation_args = { |
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"max_new_tokens": 500, |
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"return_full_text": False, |
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"temperature": 0.0, |
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"do_sample": False, |
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} |
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output = pipe(messages, **generation_args) |
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print(output[0]['generated_text']) |
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``` |
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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_dnhkng__Medium) |
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| Metric |Value| |
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|-------------------|----:| |
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|Avg. |25.94| |
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|IFEval (0-Shot) |44.06| |
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|BBH (3-Shot) |47.73| |
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|MATH Lvl 5 (4-Shot)| 7.78| |
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|GPQA (0-shot) |10.40| |
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|MuSR (0-shot) | 8.73| |
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|MMLU-PRO (5-shot) |36.96| |
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___________________________________ |
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# *SHAMELESS ADVERTISING BREAK* |
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I’m on the hunt for new challenges and a chance to dive into some exciting research opportunities. Oh, and did I mention I just snagged a top spot on the Open LLM leaderboard? 🎉 |
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#### Profile |
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Innovation enthusiast, AI strategist, and interdisciplinary-tech nerd – that's me! With over a decade of experience in research and project management, my professional journey has been largely shaped by my passion for artificial intelligence and its potential to transform various industries. With a solid background in artificial intelligence and machine learning, coupled with a knack for innovation and problem-solving (and a healthy dose of curiosity), I'm excited to bring my skills to a new team. |
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Originally from Australia, where I earned my degrees in Organic Chemistry and Biochemistry, I moved to Germany in 2004. My academic pursuit continued with a PhD in Chemistry at the Max Planck Institute of Biochemistry. Today, I leverage my robust educational background and diverse industry experience to drive AI innovations in a wide range of applications. Hobbies? Lots: I've also built the world's most powerful espresso machine and am working to bring [GLaDOS to life](https://github.com/dnhkng/GlaDOS). |
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___________________________________ |
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I'm based out of Munich, Germany, but I would be interested in working remotely for a team with more compute than my 2x 4090s 🚀 |
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#### Reach out via [LinkedIn - Dr David Noel Ng](https://www.linkedin.com/in/dnhkng) |
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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_dnhkng__RYS-Medium) |
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| Metric |Value| |
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|-------------------|----:| |
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|Avg. |25.94| |
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|IFEval (0-Shot) |44.06| |
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|BBH (3-Shot) |47.73| |
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|MATH Lvl 5 (4-Shot)| 7.78| |
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|GPQA (0-shot) |10.40| |
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|MuSR (0-shot) | 8.73| |
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|MMLU-PRO (5-shot) |36.96| |
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