Update README.md

README.md

@@ -82,7 +82,6 @@ def generate_response(model, tokenizer, input_text, max_length=300, min_length=2
             num_beams=num_beams,
             no_repeat_ngram_size=2,
             do_sample=True,
-            repetition_penalty=repetition_penalty  # Added repetition_penalty
         )
 
         # Decode the generated responses
@@ -100,7 +99,7 @@ def generate_response(model, tokenizer, input_text, max_length=300, min_length=2
 
 
 # Load pre-trained model and tokenizer
-access_token = "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"
+access_token = "REPLACE_WITH_ACCESS_TOKEN"
 model_id = "Starcodium/VergilGPT2"
 tokenizer = AutoTokenizer.from_pretrained(model_id, revision="main", use_auth_token=access_token)
 model = AutoModelForCausalLM.from_pretrained(model_id, revision="main", use_auth_token=access_token)
@@ -114,7 +113,7 @@ while True:
     if input_text.lower() in ["quit", "exit"]:
         break
 
-    responses = generate_response(model, tokenizer, input_text, max_length=100, min_length=20, num_return_sequences=1, temperature=0.7, top_k=40, top_p=0.5, num_beams=1, repetition_penalty=1.2)
+    responses = generate_response(model, tokenizer, input_text, max_length=100, min_length=20, num_return_sequences=1, temperature=0.7, top_k=40, top_p=0.5, num_beams=1)
     responses = [r for r in responses if r.strip() != '']
     if responses:
         response = responses[0]