--- license: mit model-index: - name: Medium results: - 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: 44.06 name: strict accuracy - type: inst_level_strict_acc and prompt_level_strict_acc value: 44.06 name: strict accuracy source: url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/Medium 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: 47.73 name: normalized accuracy - type: acc_norm value: 47.73 name: normalized accuracy source: url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/Medium 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: 7.78 name: exact match - type: exact_match value: 7.78 name: exact match source: url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/Medium 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: 10.4 name: acc_norm - type: acc_norm value: 10.4 name: acc_norm source: url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/Medium 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: 8.73 name: acc_norm - type: acc_norm value: 8.73 name: acc_norm source: url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/Medium 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: 36.96 name: accuracy - type: acc value: 36.96 name: accuracy source: url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/Medium name: Open LLM Leaderboard --- This is a new kind of model optimization. A paper on the technique is currently being written. 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. ## Quickstart This code snippets show how to get quickly started with running the model on a GPU: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline torch.random.manual_seed(0) model_id = "dnhkng/Medium" model = AutoModelForCausalLM.from_pretrained( model_id, device_map="cuda", torch_dtype="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_id) messages = [ {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, {"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."}, {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, ] pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, ) generation_args = { "max_new_tokens": 500, "return_full_text": False, "temperature": 0.0, "do_sample": False, } output = pipe(messages, **generation_args) print(output[0]['generated_text']) ``` # [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_dnhkng__Medium) | Metric |Value| |-------------------|----:| |Avg. |25.94| |IFEval (0-Shot) |44.06| |BBH (3-Shot) |47.73| |MATH Lvl 5 (4-Shot)| 7.78| |GPQA (0-shot) |10.40| |MuSR (0-shot) | 8.73| |MMLU-PRO (5-shot) |36.96| ___________________________________ # *SHAMELESS ADVERTISING BREAK* Iā€™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? šŸŽ‰ #### Profile Innovation enthusiast, AI strategist, and interdisciplinary-tech nerd ā€“ 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. 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). ___________________________________ 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 šŸš€ #### Reach out via [LinkedIn - Dr David Noel Ng](https://www.linkedin.com/in/dnhkng) # [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_dnhkng__RYS-Medium) | Metric |Value| |-------------------|----:| |Avg. |25.94| |IFEval (0-Shot) |44.06| |BBH (3-Shot) |47.73| |MATH Lvl 5 (4-Shot)| 7.78| |GPQA (0-shot) |10.40| |MuSR (0-shot) | 8.73| |MMLU-PRO (5-shot) |36.96|