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import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
from huggingface_hub import snapshot_download
# === Base & adapter config ===
BASE_MODEL = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
ADAPTER_PATH = "GilbertAkham/deepseek-R1-multitask-lora"
# === System message (always prepended to input) ===
SYSTEM_PROMPT = (
"You are Chat-Bot, a helpful and logical assistant trained for reasoning, "
"email, chatting, summarization, story continuation, and report writing.\n\n"
)
class EndpointHandler:
def __init__(self, path=""):
print("🚀 Loading base model...")
self.tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
print(f"🔗 Downloading LoRA adapter from {ADAPTER_PATH}...")
adapter_local_path = snapshot_download(repo_id=ADAPTER_PATH, allow_patterns=["*adapter*"])
print(f"📁 Adapter files cached at {adapter_local_path}")
print("🧩 Attaching LoRA adapter...")
self.model = PeftModel.from_pretrained(base_model, adapter_local_path)
self.model.eval()
print("✅ Model + LoRA adapter loaded successfully.")
def __call__(self, data):
# === Combine system + user prompt ===
prompt = data.get("inputs", "")
full_prompt = SYSTEM_PROMPT + prompt
params = data.get("parameters", {})
max_new_tokens = params.get("max_new_tokens", 512)
temperature = params.get("temperature", 0.7)
top_p = params.get("top_p", 0.9)
# === Tokenize and run generation ===
inputs = self.tokenizer(full_prompt, return_tensors="pt").to(self.model.device)
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
pad_token_id=self.tokenizer.eos_token_id,
eos_token_id=self.tokenizer.eos_token_id,
)
text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return {"generated_text": text}
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