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@@ -4,10 +4,10 @@ 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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  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
@@ -35,8 +35,8 @@ MoMo-70B is trained using **[Moreh](https://moreh.io/)**'s [MoAI platform](https
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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-V1.4")
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  model = AutoModelForCausalLM.from_pretrained(
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- "moreh/MoMo-70B-LoRA-V1.4"
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  )
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  ```
 
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  - en
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  ---
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  # **Introduction**
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+ MoMo-72B 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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  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-72B 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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  import torch
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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+ tokenizer = AutoTokenizer.from_pretrained("moreh/MoMo-72B-LoRA-V1.4")
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  model = AutoModelForCausalLM.from_pretrained(
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+ "moreh/MoMo-72B-LoRA-V1.4"
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  )
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  ```