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--- |
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language: |
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- nl |
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license: cc-by-nc-4.0 |
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tags: |
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- alignment-handbook |
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- generated_from_trainer |
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- trl |
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- dpo |
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- geitje |
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- conversational |
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base_model: BramVanroy/GEITje-7B-ultra-sft |
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datasets: |
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- BramVanroy/ultra_feedback_dutch |
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pipeline_tag: text-generation |
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inference: false |
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model-index: |
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- name: BramVanroy/GEITje-7B-ultra |
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results: [] |
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--- |
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<p align="center" style="margin:0;padding:0"> |
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<img src="https://huggingface.co/BramVanroy/GEITje-7B-ultra/resolve/main/geitje-ultra-banner.png" alt="GEITje Ultra banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> |
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</p> |
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<div style="margin:auto; text-align:center"> |
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<h1 style="margin-bottom: 0">GEITje 7B ultra</h1> |
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<em>A conversational model for Dutch, aligned through AI feedback.</em> |
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</div> |
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This model is a fine-tuned version of [BramVanroy/GEITje-7B-ultra-sft](https://huggingface.co/BramVanroy/GEITje-7B-ultra-sft) on a synthetic DPO dataset of around 56M tokens that was generated with gpt-4-turbo and [Rijgersberg/GEITje-7B-chat](https://huggingface.co/Rijgersberg/GEITje-7B-chat) for Dutch. |
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> [!TIP] |
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> 🚀 Looking for the fast GGUF version? You can find it, and how to use it with `ollama`, [here](https://huggingface.co/BramVanroy/GEITje-7B-ultra-GGUF). 🚀 |
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## Model description |
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This is a Dutch instruction/chat model ultimately based on Mistral and aligned with AI feedback via DPO. It is a DPO continuation of the SFT trained [BramVanroy/GEITje-7B-ultra-sft](https://huggingface.co/BramVanroy/GEITje-7B-ultra-sft), which in turn is based on [Rijgersberg/GEITje-7B](https://huggingface.co/Rijgersberg/GEITje-7B), which in turn is based on Mistral 7B and further pretrained on Dutch data. In (rather naive) [benchmarks](https://huggingface.co/spaces/BramVanroy/open_dutch_llm_leaderboard) it outperforms all the original GEITje models on average (but barely). However, note that these benchmarks should be taken with a massive grain of salt (see the disclaimer below the benchmarks on that page). The best evaluation is to try the models and see for yourself. |
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## Usage |
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One-off: |
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```python |
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from transformers import pipeline, Conversation |
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# load_in_8bit: lower precision but saves a lot of GPU memory |
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# device_map=auto: loads the model across multiple GPUs |
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chatbot = pipeline("conversational", model="BramVanroy/GEITje-7B-ultra", model_kwargs={"load_in_8bit": True}, device_map="auto") |
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start_messages = [ |
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{"role": "system", "content": "Je bent een grappige chatbot die Bert heet. Je maakt vaak mopjes."}, |
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{"role": "user", "content": "Hallo, ik ben Bram. Ik wil vanavond graag een film kijken. Heb je enkele suggesties?"} |
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] |
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conversation = Conversation(start_messages) |
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conversation = chatbot(conversation) |
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response = conversation.messages[-1]["content"] |
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print(response) |
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``` |
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Interactive conversation: |
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```python |
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from transformers import pipeline, Conversation |
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# load_in_8bit: lower precision but saves a lot of memory |
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# device_map=auto: loads the model across multiple GPUs |
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# attn_implementation: uses flash attention, if your device supports it - otherwise remove it |
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chatbot = pipeline("conversational", model="BramVanroy/GEITje-7B-ultra", model_kwargs={"load_in_8bit": True, "attn_implementation": "flash_attention_2"}, device_map="auto") |
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while (system_message := input("System message ('q' to quit): ")) != "q": |
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start_messages = [ |
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{"role": "system", "content": system_message}, |
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] |
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conversation = Conversation(start_messages) |
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while (user_input := input("User ('r' to reset): ")) != "r": |
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conversation.add_user_input(user_input) |
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conversation = chatbot(conversation) |
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response = conversation.messages[-1]["content"] |
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print("Assistant:", response) |
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``` |
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## Intended uses & limitations |
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Although the model has been aligned with gpt-4-turbo output, which has strong content filters, the model could still generate wrong, misleading, and potentially even offensive content. Use at your own risk. |
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Because the model was trained on synthetic data created with OpenAI/Azure services, this model cannot be used for commercial purposes. |
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## Training and evaluation data |
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The training data consists of a synthetic dataset based on [UltraFeedback binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) created with gpt-4-turbo and geitje-chat. A given prompt, translated from the original dataset, is given to the two models who then generated an answer. Then, gpt-4-turbo is always selected as the best answer which DPO will optimise for. While this is not completely fair, I did not have the budget to actually have gpt-4 rate both replies. Furthermore, while an impressive model, GEITje chat still seems behind gpt-4-turbo in the testing that I have done. |
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In total the dataset consists of 56,137,090 tokens (combination of prompt + rejected + chosen) and a test set of 6,178,969 tokens (11.00%). |
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## Training procedure |
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The great [alignment handbook](https://github.com/huggingface/alignment-handbook/) was used for training, with a custom slurm script for compatibility with our cluster. It was trained in full, without LoRA or other adapters. |
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The model was trained in bfloat16 with flash attention 2 on two nodes of four A100 80GB each for around 11 hours. I thank the [Flemish Super Computer](https://www.vscentrum.be/compute) for their compute. |
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For conversational usage, the model relies on the Zephyr chat template, which is compatible with system messages. A small portion of the data of *-sft contained system messages, so it is assumed the model can handle system messages at least a little bit. |
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In earlier iterations I found that using the alignment handbook's defaults (beta=0.01) led to poor results (hallucinations of random tokens). After investigating, it seems that such a low beta does not work well for this dataset as it gives the model too much room to deviate from its initial base model. After a [hyperparameter search](https://huggingface.co/posts/BramVanroy/492522322273746) and manual analysis of the resulting metrics, I selected the current model as the best one, with a beta of 0.1. |
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Recipe used with the handbook: |
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```yaml |
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# Model arguments |
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model_name_or_path: BramVanroy/GEITje-7B-ultra-sft |
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model_revision: main |
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torch_dtype: bfloat16 |
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use_flash_attention_2: true |
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# Data training arguments |
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# For definitions, see: src/h4/training/config.py |
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dataset_mixer: |
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BramVanroy/ultra_feedback_dutch: 1.0 |
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dataset_splits: |
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- train_prefs |
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- test_prefs |
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preprocessing_num_workers: 8 |
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# DPOTrainer arguments |
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bf16: true |
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beta: 0.1 |
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do_eval: true |
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evaluation_strategy: steps |
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eval_steps: 100 |
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gradient_accumulation_steps: 4 |
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gradient_checkpointing: true |
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gradient_checkpointing_kwargs: |
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use_reentrant: False |
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hub_model_id: BramVanroy/GEITje-ultra |
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learning_rate: 5.0e-7 |
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log_level: info |
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logging_steps: 10 |
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lr_scheduler_type: cosine |
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max_length: 2048 |
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max_prompt_length: 1536 |
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num_train_epochs: 1 |
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optim: adamw_torch |
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output_dir: data/GEITje-ultra |
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per_device_train_batch_size: 4 |
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per_device_eval_batch_size: 4 |
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push_to_hub: true |
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save_strategy: "steps" |
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save_steps: 100 |
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save_total_limit: 3 |
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seed: 42 |
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warmup_ratio: 0.1 |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-07 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| |
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| 0.03 | 0.22 | 100 | 0.0260 | -0.9740 | -9.8635 | 0.9913 | 8.8895 | -524.8940 | -508.1891 | -3.0753 | -3.0315 | |
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| 0.0184 | 0.44 | 200 | 0.0164 | -1.7162 | -12.4772 | 0.9926 | 10.7610 | -551.0317 | -515.6115 | -3.0349 | -2.9873 | |
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| 0.0121 | 0.66 | 300 | 0.0142 | -2.0575 | -13.6818 | 0.9938 | 11.6244 | -563.0778 | -519.0242 | -3.0325 | -2.9835 | |
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| 0.0198 | 0.88 | 400 | 0.0139 | -2.1431 | -13.8857 | 0.9950 | 11.7426 | -565.1163 | -519.8801 | -3.0293 | -2.9801 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.15.0 |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) |
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Results for the English Open LLM Leaderboard. For results specific to Dutch, check out [ScandEval](https://scandeval.com/dutch-nlg/). |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BramVanroy__GEITje-7B-ultra) |
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| Metric |Value| |
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|-------------------|----:| |
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|Avg. |10.91| |
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|IFEval (0-Shot) |37.23| |
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|BBH (3-Shot) |12.88| |
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|MATH Lvl 5 (4-Shot)| 0.91| |
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|GPQA (0-shot) | 1.68| |
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|MuSR (0-shot) | 1.52| |
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|MMLU-PRO (5-shot) |11.24| |
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