Update README.md

README.md

@@ -1,3 +1,57 @@
 ---
 license: apache-2.0
+datasets:
+- Anthropic/hh-rlhf
+language:
+- en
+pipeline_tag: text-generation
 ---
+
+
+# GPT-2 Medium SFT and DPO on Anthropic-hh Dataset
+
+This repository GPT-2 Medium model instruct tuned first on the Anthropic-hh dataset and then further aligned on the same dataset with DPO. 
+
+## Model Information
+
+- **Model Name:** RaushanTurganbay/GPT2_sft_and_dpo_tuned
+- **Base Model:** GPT-2 Medium
+- **Training Data:** Anthropic-hh dataset
+- **Fine-Tuning Approach:** Direct Preference Optiization (DPO)
+
+## How to Use
+
+
+```python
+import torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer, StoppingCriteria, StoppingCriteriaList
+
+tokenizer_dpo = GPT2Tokenizer.from_pretrained("RaushanTurganbay/GPT2_sft_and_dpo_tuned")
+model_dpo = GPT2LMHeadModel.from_pretrained("RaushanTurganbay/GPT2_sft_and_dpo_tuned")
+
+class StoppingCriteriaSub(StoppingCriteria):
+    def __init__(self, stops=[], encounters=1):
+        super().__init__()
+        self.stops = [stop.to("cuda") for stop in stops]
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
+        for stop in self.stops:
+            if torch.all((stop == input_ids[0][-len(stop):])).item():
+                return True
+        return False
+
+
+def stopping_criteria(tokenizer, stop_words):
+    stop_words_ids = [tokenizer(stop_word, return_tensors='pt')['input_ids'].squeeze() for stop_word in stop_words]
+    stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])
+    return stopping_criteria
+
+
+# Generate responses
+stopping = stopping_criteria(tokenizer, ["\n\nHuman:"])
+prompt = "\n\nHuman: {your_instruction}\n\nAssistant:"
+inputs_dpo = tokenizer_dpo(prompt, return_tensors="pt")
+outputs_dpo = model_dpo.generate(**inputs_dpo,  stopping_criteria=stopping, max_length=150)
+
+print("Model Response:", tokenizer_dpo.batch_decode(outputs_dpo))
+```
+