Update README.md

README.md

@@ -11,6 +11,7 @@ model-index:
   results: []
 datasets:
 - HuggingFaceH4/ultrafeedback_binarized
+- ayoubkirouane/Orca-Direct-Preference-Optimization
 pipeline_tag: text-generation
 ---
 
@@ -24,28 +25,4 @@ pipeline_tag: text-generation
 
 Direct Preference Optimization (DPO) is an algorithm introduced in order to achieve precise control of the behavior of large-scale unsupervised language models (LMs). It is a parameterization of the reward model in Reinforcement Learning from Human Feedback (RLHF) that enables the extraction of the corresponding optimal policy in closed form. This allows for the solution of the standard RLHF problem with only a simple classification loss.
 
-DPO eliminates the need for sampling from the LM during fine-tuning or performing significant hyperparameter tuning, making it stable, performant, and computationally lightweight. Experiments have shown that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods. It has been found to be particularly effective in controlling the sentiment of generations and matching or improving response quality in summarization and single-turn dialogue.
-
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 5e-06
-- train_batch_size: 2
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 8
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 1
-- mixed_precision_training: Native AMP
-
-### Framework versions
-
-- PEFT 0.7.1
-- Transformers 4.38.0.dev0
-- Pytorch 2.1.2
-- Datasets 2.16.1
-- Tokenizers 0.15.0
+DPO eliminates the need for sampling from the LM during fine-tuning or performing significant hyperparameter tuning, making it stable, performant, and computationally lightweight. Experiments have shown that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods. It has been found to be particularly effective in controlling the sentiment of generations and matching or improving response quality in summarization and single-turn dialogue.