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Running on Zero
Running on Zero
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import spaces
import torch
from transformers import (
AutoTokenizer,
AutoModelForCausalLM
)
from peft import PeftModel
# ============================================================
# CONFIGURATION
# ============================================================
BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
ADAPTER_MODEL = "dd253B/DhanushAI-0.5B"
# ============================================================
# GLOBAL MODEL
# ============================================================
tokenizer = None
model = None
# ============================================================
# LOAD MODEL
# ============================================================
def load_model():
global tokenizer
global model
if model is not None:
return
print("====================================")
print("Loading DhanushAI...")
print("====================================")
# -----------------------------
# Tokenizer
# -----------------------------
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(
BASE_MODEL
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# -----------------------------
# Base model
# -----------------------------
print("Loading base model...")
base_model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
torch_dtype=torch.float16
)
# -----------------------------
# LoRA adapter
# -----------------------------
print("Loading DhanushAI adapter...")
model = PeftModel.from_pretrained(
base_model,
ADAPTER_MODEL
)
# -----------------------------
# Move to GPU
# -----------------------------
model = model.to("cuda")
model.eval()
print("====================================")
print("DhanushAI loaded successfully!")
print("====================================")
# ============================================================
# CHAT FUNCTION
# ============================================================
@spaces.GPU
def chat(message):
# Load model after ZeroGPU allocation
load_model()
if message is None:
return "Please enter a message."
message = message.strip()
if not message:
return "Please enter a message."
# -----------------------------
# Prompt
# -----------------------------
prompt = f"""You are DhanushAI, a helpful AI assistant.
User: {message}
Assistant:"""
# -----------------------------
# Tokenize
# -----------------------------
inputs = tokenizer(
prompt,
return_tensors="pt"
)
inputs = {
key: value.to("cuda")
for key, value in inputs.items()
}
# -----------------------------
# Generate
# -----------------------------
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True,
repetition_penalty=1.1
)
# -----------------------------
# Decode
# -----------------------------
generated = tokenizer.decode(
outputs[0],
skip_special_tokens=True
)
# -----------------------------
# Remove prompt
# -----------------------------
if "Assistant:" in generated:
answer = generated.split(
"Assistant:",
1
)[1].strip()
else:
answer = generated.strip()
return answer
# ============================================================
# GRADIO UI + API
# ============================================================
demo = gr.Interface(
fn=chat,
inputs=gr.Textbox(
label="Message",
placeholder="Ask DhanushAI something..."
),
outputs=gr.Textbox(
label="DhanushAI"
),
title="DhanushAI",
description="My custom AI model",
api_name="chat"
)
# ============================================================
# START
# ============================================================
demo.launch() |