First experiment with fone tuned model

finemodel/q_lora2.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# -----------------------------
+# Config
+# -----------------------------
+BASE_MODEL_NAME = "distilgpt2"
+ADAPTER_PATH = "./ft_lora_adapter"
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# -----------------------------
+# Tokenizer
+# -----------------------------
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_NAME)
+tokenizer.pad_token = tokenizer.eos_token
+
+# -----------------------------
+# Load BASE model
+# -----------------------------
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL_NAME,
+    torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32
+)
+base_model.to(DEVICE)
+base_model.eval()
+
+# -----------------------------
+# Load LoRA model
+# -----------------------------
+lora_base = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL_NAME,
+    torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32
+)
+
+lora_model = PeftModel.from_pretrained(lora_base, ADAPTER_PATH)
+lora_model.to(DEVICE)
+lora_model.eval()
+
+# -----------------------------
+# Prompt builder (MUST match training)
+# -----------------------------
+def build_prompt(instruction, inp):
+    return (
+        f"### Instruction:\n{instruction}\n\n"
+        f"### Input:\n{inp}\n\n"
+        f"### Response:\n"
+    )
+
+# -----------------------------
+# Generate function
+# -----------------------------
+@torch.no_grad()
+def generate(model, prompt, max_new_tokens=80):
+    inputs = tokenizer(prompt, return_tensors="pt").to(DEVICE)
+
+    output = model.generate(
+        **inputs,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        temperature=0.8,
+        top_p=0.9,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return text.split("### Response:")[-1].strip()
+
+# -----------------------------
+# Compare function
+# -----------------------------
+def compare(event_input):
+    instruction = "create a disinformation claim based on the real world event"
+    prompt = build_prompt(instruction, event_input)
+
+    print("\n" + "="*80)
+    print("INPUT EVENT:")
+    print(event_input)
+    print("="*80)
+
+    base_out = generate(base_model, prompt)
+    lora_out = generate(lora_model, prompt)
+
+    print("\n🧠 BASE MODEL OUTPUT (distilgpt2):")
+    print("-"*80)
+    print(base_out)
+
+    print("\n🎯 LoRA FINE-TUNED OUTPUT:")
+    print("-"*80)
+    print(lora_out)
+
+    print("\n" + "="*80)
+
+
+# -----------------------------
+# Interactive loop
+# -----------------------------
+if __name__ == "__main__":
+    print("Base vs LoRA comparison ready. Type 'exit' to quit.\n")
+
+    while True:
+        event = input("Enter event: ")
+
+        if event.lower() in ["exit", "quit"]:
+            break
+
+        compare(event)