Update README.md

README.md

@@ -2,13 +2,13 @@
 license: apache-2.0
 ---
 
-# Phi-3-mini-128K-instruct with CPO-SimPO
+# Phi-3-mini-4K-instruct with CPO-SimPO
 
 This repository contains the Phi-3-mini-128K-instruct model enhanced with the CPO-SimPO technique. CPO-SimPO combines Contrastive Preference Optimization (CPO) and Simple Preference Optimization (SimPO).
 
 ## Introduction
 
-Phi-3-mini-128K-instruct is a model optimized for instruction-based tasks. This approach has demonstrated notable improvements in key benchmarks, pushing the boundaries of AI preference learning.
+Phi-3-mini-4K-instruct is a model optimized for instruction-based tasks. This approach has demonstrated notable improvements in key benchmarks, pushing the boundaries of AI preference learning.
 
 ### What is CPO-SimPO?
 
@@ -26,8 +26,6 @@ CPO-SimPO is a novel technique, which combines elements from CPO and SimPO:
 
 COMING SOON!
 
-- **TruthfulQA:** 56.19
-
 ### Key Improvements:
 - **Enhanced Model Performance:** Significant score improvements, particularly in GSM8K (up by 8.49 points!) and TruthfulQA (up by 2.07 points).
 - **Quality Control:** Improved generation of high-quality sequences through length normalization and reward margins.
@@ -54,12 +52,12 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 torch.random.manual_seed(0)
 
 model = AutoModelForCausalLM.from_pretrained(
-    "Syed-Hasan-8503/Phi-3-mini-128K-instruct-cpo-simpo", 
+    "Syed-Hasan-8503/Phi-3-mini-4K-instruct-cpo-simpo", 
     device_map="cuda", 
     torch_dtype="auto", 
     trust_remote_code=True, 
 )
-tokenizer = AutoTokenizer.from_pretrained("Syed-Hasan-8503/Phi-3-mini-128K-instruct-cpo-simpo")
+tokenizer = AutoTokenizer.from_pretrained("Syed-Hasan-8503/Phi-3-mini-4K-instruct-cpo-simpo")
 
 messages = [
     {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},