Update README.md

README.md

@@ -48,31 +48,31 @@ import torch
 from unsloth import FastLanguageModel
 from transformers import AutoTokenizer
 
-# Load model and tokenizer
+Load model and tokenizer
 model_path = "moo100/DeepSeek-R1-telecom-chatbot"
 model, tokenizer = FastLanguageModel.from_pretrained(model_path, max_seq_length=1024, dtype=None)
 
-# Optimize for fast inference
+Optimize for fast inference
 model = FastLanguageModel.for_inference(model)
 
-# Move model to GPU if available
+Move model to GPU if available
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model.to(device)
 
-# Define system instruction for guided response
+Define system instruction for guided response
 system_instruction = """You are an AI assistant. Answer user questions concisely and factually.
 Do NOT role-play as a customer service agent. Only answer the user's query."""
 
-# Define user input
+Define user input
 user_input = "What are the benefits of 5G?"
 
-# Construct full prompt
+Construct full prompt
 full_prompt = f"{system_instruction}\n\nUser: {user_input}\nAssistant:"
 
-# Tokenize input
+Tokenize input
 inputs = tokenizer(full_prompt, return_tensors="pt").to(device)
 
-# Generate response
+Generate response
 outputs = model.generate(
     input_ids=inputs.input_ids,
     attention_mask=inputs.attention_mask,
@@ -83,7 +83,7 @@ outputs = model.generate(
     eos_token_id=tokenizer.eos_token_id,  
 )
 
-# Decode and print response
+Decode and print response
 response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
 print(response.strip())
 
@@ -94,7 +94,7 @@ print(response.strip())
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-
+talkmap/telecom-conversation-corpus
 
 ### Training Procedure