Create run_model.py

run_model.py

@@ -0,0 +1,39 @@
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+model = GPT2LMHeadModel.from_pretrained('gpt2')
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+tokenizer.add_special_tokens({'eos_token': '<|End|>'})
+special_tokens = {
+    "additional_special_tokens": ["<|USER|>", "<|SYSTEM|>", "<|ASSISTANT|>"]
+}
+tokenizer.add_special_tokens(special_tokens)
+model.resize_token_embeddings(len(tokenizer))
+model.load_state_dict(torch.load("pytorch_model.bin"))
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+def generate_text(model, tokenizer, prompt, max_length=1024):
+    prompt = f'<|SYSTEM|> You are a helpful AI designed to answer questions <|USER|> {prompt} <|ASSISTANT|> '
+    input_ids = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt").to(device)
+    attention_mask = torch.ones_like(input_ids).to(device)
+    output = model.generate(input_ids, 
+                            max_length=max_length, 
+                            do_sample=True, 
+                            top_k=50, 
+                            top_p=0.30,
+                            pad_token_id=tokenizer.pad_token_id,
+                            eos_token_id=tokenizer.eos_token_id,
+                            attention_mask=attention_mask)
+    output_ids = tokenizer.decode(output[0], skip_special_tokens=False)
+    assistant_token_index = output_ids.index('<|ASSISTANT|>') + len('<|ASSISTANT|>')
+    next_token_index = output_ids.find('<|', assistant_token_index)
+    output_ids = output_ids[assistant_token_index:next_token_index]
+    return output_ids
+# Loop to interact with the model
+while True:
+    prompt = input("Enter a prompt (or 'q' to quit): ")
+    if prompt == "q":
+        break
+    output_text = generate_text(model, tokenizer, prompt)
+    print(output_text)