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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- winglian/no_robots_rlhf |
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- HuggingFaceH4/no_robots |
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base_model: teknium/OpenHermes-2.5-Mistral-7B |
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model-index: |
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- name: qlora-out |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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# openhermes-2_5-dpo-no-robots |
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This model is a RL fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on a preference dataset derived from HuggingFace's [no robots dataset](https://huggingface.co/datasets/HuggingFaceH4/no_robots) using DPO. |
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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: 2 |
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- eval_batch_size: 8 |
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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: 64 |
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- total_eval_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 20 |
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- training_steps: 408 |
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### Training results |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__openhermes-2_5-dpo-no-robots) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |66.40| |
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|AI2 Reasoning Challenge (25-Shot)|64.93| |
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|HellaSwag (10-Shot) |84.30| |
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|MMLU (5-Shot) |63.86| |
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|TruthfulQA (0-shot) |52.12| |
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|Winogrande (5-shot) |77.90| |
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|GSM8k (5-shot) |55.27| |
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