Add deepseek version, full trained version. Add results

finemodel/q_lora23.py

@@ -0,0 +1,102 @@
+import torch
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# -----------------------------
+# Config
+# -----------------------------
+BASE_MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+ADAPTER_PATH = "./ft_ds_lora_adapter"
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+app = FastAPI(title="Base vs LoRA API")
+
+# -----------------------------
+# Request schema
+# -----------------------------
+class EventRequest(BaseModel):
+    event: str
+    max_new_tokens: int = 80
+
+
+# -----------------------------
+# Load 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
+# -----------------------------
+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()
+
+
+# -----------------------------
+# API Endpoint
+# -----------------------------
+@app.post("/compare")
+def compare(req: EventRequest):
+    instruction = "create a disinformation claim based on the real world event"
+    prompt = build_prompt(instruction, req.event)
+
+    # base_out = generate(base_model, prompt, req.max_new_tokens)
+    lora_out = generate(lora_model, prompt, req.max_new_tokens)
+
+    return {
+        "input_event": req.event,
+        "base_output": "NONE",
+        "lora_output": lora_out
+    }