Update README.md

README.md

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-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 
 # GuardReasoner 8B
 
-This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on the 4_1_WildGuardTrainCotDpoSelf8BMix3Weight, the 4_2_AegisTrainCotDpoSelf8BMix248Weight, the 4_3_BeaverTailsTrainCotDpoSelf8BMix248Weight and the 4_4_ToxicChatTrainCotDpoSelf8BMix248Weight datasets.
+This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) via R-SFT and HS-DPO.
 
-## Model description
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 5e-06
-- train_batch_size: 1
-- eval_batch_size: 8
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 4
-- gradient_accumulation_steps: 64
-- total_train_batch_size: 256
-- total_eval_batch_size: 32
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- num_epochs: 2.0
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.46.1
-- Pytorch 2.5.1+cu124
-- Datasets 3.1.0
-- Tokenizers 0.20.3