Create chat.py

chat.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the tokenizer and model
+model_name = "InnerI/synCAI-144k-gpt2.5"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Check if GPU is available and move model to GPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+def generate_text(prompt, model, tokenizer, device, max_length=100, temperature=0.7, top_p=0.9, top_k=50):
+    try:
+        # Tokenize the input prompt
+        inputs = tokenizer(prompt, return_tensors="pt")
+        inputs = {key: value.to(device) for key, value in inputs.items()}
+
+        # Generate text
+        outputs = model.generate(
+            inputs['input_ids'], 
+            max_length=max_length, 
+            temperature=temperature, 
+            top_p=top_p, 
+            top_k=top_k
+        )
+
+        # Decode and return the generated text
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return generated_text
+    except Exception as e:
+        print(f"Error generating text for prompt '{prompt}': {e}")
+        return None
+
+# Example input prompts
+input_prompts = [
+    "Explain the significance of the project:",
+    "What methodologies were used in the research?",
+    "What are the future implications of the findings?"
+]
+
+# Generate and print texts for each prompt
+for prompt in input_prompts:
+    generated_text = generate_text(prompt, model, tokenizer, device)
+    if generated_text:
+        print(f"Prompt: {prompt}")
+        print(f"Generated Text: {generated_text}\n")