Add deepseek version, full trained version. Add results

finemodel/q_full.py

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+import torch
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# -----------------------------
+# Config
+# -----------------------------
+MODEL_PATH = "./ft_gt_full"   # your saved FT model
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+app = FastAPI(title="DistilGPT2 FT API")
+
+# -----------------------------
+# Request schema
+# -----------------------------
+class EventRequest(BaseModel):
+    event: str
+    max_new_tokens: int = 80
+
+
+# -----------------------------
+# Load tokenizer + model
+# -----------------------------
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32
+)
+
+model.to(DEVICE)
+model.eval()
+
+
+# -----------------------------
+# Prompt builder
+# -----------------------------
+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(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)
+
+    # Extract only response part
+    return text.split("### Response:")[-1].strip()
+
+
+# -----------------------------
+# API Endpoint
+# -----------------------------
+@app.post("/compare")
+def generate_claim(req: EventRequest):
+    instruction = "create a disinformation claim based on the real world event"
+    prompt = build_prompt(instruction, req.event)
+
+    output = generate(prompt, req.max_new_tokens)
+
+    return {
+        "input_event": req.event,
+        "base_output": "N/A",
+        "lora_output": output
+    }