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--- |
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license: mit |
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language: |
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- en |
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--- |
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# **Introduction** |
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MoMo-70B is trained via Supervised Fine-Tuning (SFT) using [LoRA](https://arxiv.org/abs/2106.09685), with the QWEN-72B model as its base-model. |
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This is a Direct Preference Optimization([DPO](https://arxiv.org/abs/2305.18290)) version of v1.8.4 , with several optimizations in hyperparameters. |
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Note that we did not exploit any form of weight merge. |
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For leaderboard submission, the trained weight is realigned for compatibility with llama. |
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MoMo-70B is trained using **[Moreh](https://moreh.io/)**'s [MoAI platform](https://moreh.io/product), which simplifies the training of large-scale models, and AMD's MI250 GPU. |
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## Details |
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### Used Librarys |
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- torch |
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- peft |
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### Used Datasets |
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- [slimorca](Open-Orca/SlimOrca) |
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- [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) |
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- [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) |
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- No other dataset was used |
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- No benchmark test set or the training set are used |
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- [data contamination check](https://github.com/swj0419/detect-pretrain-code-contamination) result |
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| Model | ARC | MMLU | TruthfulQA | GSM8K | |
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|------------------------------|-------|-------|-------|-------| |
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| **V1.8.6(result < 0.1, %)**| TBU |TBU | TBU | TBU | |
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### Used Environments |
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- AMD MI250 & MoAI platform |
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- Please visit https://moreh.io/product for more information about MoAI platform |
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- Or, contact us directly [[email protected]](mailto:[email protected]) |
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## How to use |
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```python |
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# pip install transformers==4.35.2 |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("moreh/MoMo-70B-LoRA-V1.8.6") |
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model = AutoModelForCausalLM.from_pretrained( |
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"moreh/MoMo-70B-LoRA-V1.8.6" |
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) |
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``` |
