suriya7
/

ChatGPT-2.V2

@@ -3,197 +3,98 @@ library_name: transformers
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 tags: []
 ---
+### Inference
+```python
+# Load model directly
+from transformers import AutoModelForCausalLM, GPT2Tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained("suriya7/ChatGPT-2.V2")
+model = AutoModelForCausalLM.from_pretrained("suriya7/ChatGPT-2.V2")
+```
+### Chatting
+```python
+import torch
+prompt = """
+<|im_start|>system\nYou are a helpful AI assistant named Securitron, trained by Aquilax.<|im_end|>
+"""
+# Keep a list for the last one conversation exchanges
+conversation_history = []
+while True:
+    user_prompt = input("User Question: ")
+    if user_prompt.lower() == 'break':
+        break
+    # Format the user's input
+    user = f"""<|im_start|>user
+{user_prompt}<|im_end|>"""
+    # Add the user's question to the conversation history
+    conversation_history.append(user)
+    # Ensure conversation starts with a user's input and keep only the last 2 exchanges (4 turns)
+    conversation_history = conversation_history[-5:]
+    # Build the full prompt
+    current_prompt = prompt + "\n".join(conversation_history)
+    # Tokenize the prompt
+    encodeds = tokenizer(current_prompt, return_tensors="pt", truncation=True).input_ids
+    # Move model and inputs to the appropriate device
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    inputs = encodeds.to(device)
+    # Create an empty list to store generated tokens
+    generated_ids = inputs
+    # Start generating tokens one by one
+    assistant_response = ""
+    # print("Assistant: ", end="", flush=True)  # Print "Assistant:" once before streaming starts
+    for _ in range(512):  # Specify a max token limit for streaming
+        # Generate the next token in the sequence
+        next_token = model.generate(
+            generated_ids,
+            max_new_tokens=1,
+            pad_token_id=50259,
+            eos_token_id=50259,
+            num_return_sequences=1,
+            do_sample=True,  # Use sampling for more diverse responses
+            top_k=50,        # Limit to the top-k tokens to sample from
+            temperature=0.7, # Adjust temperature for randomness
+            top_p =0.90
+        )
+        # Add the generated token to the list
+        generated_ids = torch.cat([generated_ids, next_token[:, -1:]], dim=1)
+        # Decode the generated token (flatten it to a list of IDs)
+        token_id = next_token[0, -1].item()  # Extract the last token as an integer
+        token = tokenizer.decode([token_id], skip_special_tokens=True)
+        # Append the token to the ongoing response
+        assistant_response += token
+        print(token, end="", flush=True)  # Stream the token in real time
+        # If EOS token is encountered, stop generating
+        if token_id == 50259:  # EOS token
+            break
+    print()  # Print a newline after streaming is complete
+    # Add the assistant's response to the conversation history
+    conversation_history.append(f"<|im_start|>{assistant_response.strip()}<|im_end|>")
+```