Update README.md

README.md

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 license: mit
+language:
+- en
 ---
+# **Introduction**
+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.  
+This is a Direct Preference Optimization([DPO](https://arxiv.org/abs/2305.18290)) version of v1.8.4 , with several optimizations in hyperparameters. 
+Note that we did not exploit any form of weight merge.  
+For leaderboard submission, the trained weight is realigned for compatibility with llama.  
+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.
+
+
+## Details
+### Used Librarys
+- torch
+- peft
+### Used Datasets
+- [slimorca](Open-Orca/SlimOrca)
+- [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
+- [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
+- No other dataset was used
+- No benchmark test set or the training set are used
+  - [data contamination check](https://github.com/swj0419/detect-pretrain-code-contamination) result
+    
+| Model                        | ARC   | MMLU | TruthfulQA | GSM8K |
+|------------------------------|-------|-------|-------|-------|
+| **V1.8.5(result < 0.1, %)**| TBU |TBU | TBU | TBU |
+### Used Environments
+- AMD MI250 & MoAI platform
+- Please visit https://moreh.io/product for more information about MoAI platform
+- Or, contact us directly [[email protected]](mailto:[email protected])
+
+## How to use
+
+```python
+# pip install transformers==4.35.2
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("moreh/MoMo-70B-LoRA-V1.8.6")
+model = AutoModelForCausalLM.from_pretrained(
+    "moreh/MoMo-70B-LoRA-V1.8.6"
+)
+```