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--- |
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language: |
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- multilingual |
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license: gemma |
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library_name: transformers |
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tags: |
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- nlp |
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- code |
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base_model: google/gemma-2-2b-jpn-it |
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datasets: |
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- mlabonne/orpo-dpo-mix-40k |
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license_link: https://ai.google.dev/gemma/terms |
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pipeline_tag: text-generation |
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quantized_by: ymcki |
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widget: |
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- messages: |
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- role: user |
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content: Can you provide ways to eat combinations of bananas and dragonfruits? |
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model-index: |
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- name: gemma-2-2b-jpn-it-abliterated-17-ORPO |
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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: 49.48 |
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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=ymcki/gemma-2-2b-jpn-it-abliterated-17-ORPO |
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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: 14.92 |
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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=ymcki/gemma-2-2b-jpn-it-abliterated-17-ORPO |
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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: 2.87 |
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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=ymcki/gemma-2-2b-jpn-it-abliterated-17-ORPO |
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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: 3.24 |
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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=ymcki/gemma-2-2b-jpn-it-abliterated-17-ORPO |
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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: 5.67 |
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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=ymcki/gemma-2-2b-jpn-it-abliterated-17-ORPO |
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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: 13.18 |
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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=ymcki/gemma-2-2b-jpn-it-abliterated-17-ORPO |
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name: Open LLM Leaderboard |
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--- |
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Original model: https://huggingface.co/google/gemma-2-2b-jpn-it |
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## Prompt format |
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``` |
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<start_of_turn>user |
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{prompt}<end_of_turn> |
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<start_of_turn>model |
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<end_of_turn> |
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<start_of_turn>model |
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``` |
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Note that this model does not support a System prompt. |
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This is abliterated model of [google/gemma-2-2b-jpn-it](https://huggingface.co/google/gemma-2-2b-jpn-it) using the |
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[method](https://medium.com/@mlabonne/uncensor-any-llm-with-abliteration-d30148b7d43e) |
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described by mlabonne. |
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Layer 17 of the original model was chosen for abliteration. |
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I also created another layer 18 abliterated model for comparison. |
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ORPO fine tuning was performed for four epoches. |
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| Epoch | loss | eval_loss | |
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| ----- | ---- | --------- | |
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| 1 | 1.20152769684791564 | 1.0501047372817993 | |
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| 2 | 1.25755584239959716 | 1.0144596099853516 | |
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| 3 | 0.93099724054336543 | 0.9957754611968994 | |
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| 4 | 0.88664623498916623 | 0.9857067465782166 | |
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The fine tuned model is uploaded here to be evaluated by the Open LLM Leaderboard to see if the slightly brain damaged non-ORPO model can be healed. Again, the fine tuning method is also based on one described by [mlabonne](https://towardsdatascience.com/fine-tune-llama-3-with-orpo-56cfab2f9ada) but the input model was read into VRAM by [unsloth](https://github.com/unslothai/unsloth) to allow using the full 40k dataset to run on a single 3090. |
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## Benchmark (100.0*raw scores only) |
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Click on the model name go to the raw score json generated by Open LLM Leaderboard. |
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| Model | Average | IFEval | BHH | Math Lv5 | GPQA | MUSR | MMLU-PRO | |
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| ----- | ------- | ------ | ----|--------- | ---- | ---- | -------- | |
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| [gemma-2-2b-jpn-it](https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/google/gemma-2-2b-jpn-it/results_2024-10-15T15-21-39.173019.json) | 30.82 | 54.11 | 41.43 | 0.0 | 27.52 | 37.17 | 24.67 | |
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| [gemma-2-2b-jpn-it-abliterated-17-ORPO](https://huggingface.co/datasets/open-llm-leaderboard/results/raw/main/ymcki/gemma-2-2b-jpn-it-abliterated-17-ORPO/results_2024-10-20T02-46-59.069357.json) | 29.99 | 50.94 | 38.59 | 2.87 | 27.43 | 38.23 | 21.86 | |
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| [gemma-2-2b-jpn-it-abliterated-17](https://huggingface.co/datasets/open-llm-leaderboard/results/raw/main/ymcki/gemma-2-2b-jpn-it-abliterated-17/results_2024-10-18T15-18-46.821674.json) | 30.29 | 52.65 | 40.46 | 0.0 | 27.18 | 36.90 | 24.55 | |
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| [gemma-2-2b-jpn-it-abliterated-18](https://huggingface.co/datasets/open-llm-leaderboard/results/raw/main/ymcki/gemma-2-2b-jpn-it-abliterated-18/results_2024-10-18T15-41-42.399571.json) | 30.61 | 53.02 | 40.96 | 0.0 | 27.35 | 37.30 | 25.05 | |
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Looks like fine tuning is probably not enough. May need to run more epoches. |
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## How to run this model |
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```py |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import transformers |
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import torch |
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model_id = "gemma-2-2b-jpn-it-abliterated-17-ORPO" |
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dtype = torch.bfloat16 |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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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=dtype,) |
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chat = [ |
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{ "role": "user", "content": "Write a hello world program" }, |
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] |
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prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) |
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``` |
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## Downloading using huggingface-cli |
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First, make sure you have hugginface-cli installed: |
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``` |
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pip install -U "huggingface_hub[cli]" |
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``` |
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Then, you can target the specific file you want: |
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``` |
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huggingface-cli download ymcki/gemma-2-2b-jpn-it-abliterated-17-ORPO --include "*" --local-dir ./ |
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``` |
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## Credits |
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Thank you mlabonne for describing his fine tuning method. |
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Thanks FullOf_Bad_Ideas from LocalLlama for the suggestion of using unsloth to save VRAM. |
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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_ymcki__gemma-2-2b-jpn-it-abliterated-17-ORPO) |
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| Metric |Value| |
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|-------------------|----:| |
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|Avg. |14.89| |
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|IFEval (0-Shot) |49.48| |
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|BBH (3-Shot) |14.92| |
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|MATH Lvl 5 (4-Shot)| 2.87| |
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|GPQA (0-shot) | 3.24| |
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|MuSR (0-shot) | 5.67| |
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|MMLU-PRO (5-shot) |13.18| |
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