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HuggingFace Space — Gemma 4 Quantitative Finance Chat
Hardware: Nvidia T4 medium (16 GB VRAM)
"""
import os
import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
HF_TOKEN = os.environ.get("HF_TOKEN", "")
HF_USERNAME = os.environ.get("HF_USERNAME", "mo35")
BASE_MODEL = "google/gemma-4-E4B-it"
LORA_REPO = f"{HF_USERNAME}/gemma4-quantfin-lora"
# ── Load model at startup ─────────────────────────────────────────────────────
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, token=HF_TOKEN)
print("Loading base model in 4-bit...")
bnb_config = BitsAndBytesConfig(
load_in_4bit = True,
bnb_4bit_compute_dtype = torch.bfloat16,
bnb_4bit_use_double_quant = True,
bnb_4bit_quant_type = "nf4",
)
base_model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
quantization_config = bnb_config,
device_map = "auto",
token = HF_TOKEN,
)
print(f"Loading LoRA adapter from {LORA_REPO}...")
model = PeftModel.from_pretrained(base_model, LORA_REPO, token=HF_TOKEN)
model.eval()
print("Model ready.")
# ── Inference ─────────────────────────────────────────────────────────────────
def respond(message: str, history: list) -> str:
messages = []
for msg in history:
messages.append({"role": msg["role"], "content": msg["content"]})
messages.append({"role": "user", "content": message})
# apply_chat_template returns BatchEncoding in newer transformers
encoded = tokenizer.apply_chat_template(
messages,
add_generation_prompt = True,
return_tensors = "pt",
return_dict = True,
)
input_ids = encoded["input_ids"].to(model.device)
attention_mask = encoded.get("attention_mask", None)
if attention_mask is not None:
attention_mask = attention_mask.to(model.device)
with torch.no_grad():
outputs = model.generate(
input_ids,
attention_mask = attention_mask,
max_new_tokens = 1024,
temperature = 0.7,
do_sample = True,
repetition_penalty = 1.1,
)
return tokenizer.decode(
outputs[0][input_ids.shape[-1]:],
skip_special_tokens = True,
)
# ── Gradio UI ─────────────────────────────────────────────────────────────────
demo = gr.ChatInterface(
fn = respond,
type = "messages",
title = "Gemma 4 — Quantitative Finance",
description = (
"A specialized AI assistant fine-tuned on quantitative finance: derivatives pricing, "
"stochastic calculus, risk models, and portfolio theory. "
"Answers include LaTeX mathematical derivations."
),
examples = [
"Derive the Black-Scholes PDE from first principles.",
"Explain the SABR model and its implied volatility approximation.",
"What is the difference between risk-neutral and real-world measures?",
"Derive the Heston model characteristic function.",
"Explain Value at Risk vs Expected Shortfall.",
],
theme = gr.themes.Soft(),
cache_examples = False,
)
demo.launch()
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