VERBAREX/LuminoLex-Aura-235B-A22B

LuminoLex-Aura 235B A22B Inference Guide

Welcome to the official inference guide for LuminoLex-Aura 235B A22B, a highly optimized, fine-tuned AI model developed by VERBAREX. Built upon the powerful Qwen-3 235B A22B base model, this repository provides an ultra-fast Python script to load and run inference on this massive 235-billion parameter MoE (Mixture of Experts) model.

🚀 Overview

Running a 235B parameter model is resource-intensive. At 16-bit precision, this model would require approximately 470GB of VRAM. However, using 4-bit NF4 quantization, we compress the model down to ~130GB.

This script is specifically engineered to run entirely on a single NVIDIA H200 (141GB VRAM), utilizing bfloat16 compute types and Hugging Face's high-speed Rust-based downloader for maximum performance.

💻 Hardware Requirements

• GPU: 1x NVIDIA H200 (or equivalent setup with 141GB+ VRAM)
• System RAM: 128GB+ recommended
• Storage: 150GB+ of fast NVMe SSD storage for model weights

🛠️ Software Dependencies

The script will automatically attempt to install missing dependencies via pip. However, for a stable environment, ensure you have the following installed:

• torch (Compiled with CUDA support)
• transformers
• bitsandbytes
• accelerate
• hf_transfer

---

📜 Inference Script (inference.py)

Create a file named inference.py and paste the following code into it. You can run this directly in your terminal or inside a Jupyter/Colab notebook.

import os
import sys
import subprocess

# --- ENABLE ULTRA-FAST DOWNLOADS ---
# This tells Hugging Face to use the Rust-based multi-threaded downloader.
# It can increase download speeds by 10x-50x for massive models like this 235B model.
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"

# --- DEPENDENCY CHECK ---
# Modal/Colab notebooks might not have quantization libraries pre-installed.
# This block ensures required packages are installed before running.
try:
    import bitsandbytes
    import accelerate
    import hf_transfer
except ImportError:
    print("Missing packages. Installing 'bitsandbytes', 'accelerate', and 'hf_transfer'...")
    subprocess.check_call([sys.executable, "-m", "pip", "install", "-U", "-q", "bitsandbytes", "accelerate", "hf-transfer"])
    
    # CRITICAL FIX: Tell Python to refresh its package cache so it sees the newly installed packages
    import importlib
    import site
    importlib.invalidate_caches()
    print("Installation complete!")

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

# --- HUGGING FACE SETUP ---
# Path to your fine-tuned VERBAREX model. 
# Note: Ensure this points to the exact repo name where your fine-tune is hosted.
model_id = "Qwen/Qwen3-235B-A22B" 

print(f"Downloading/Loading fine-tuned model from Hugging Face: {model_id}...")
print("If downloading, hf_transfer is active. This will be much faster!")

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True,
    use_fast=False
)

# --- OPTIMIZED FOR 1x H200 141GB ---
# We compress the ~470GB model to 4-bit (~130GB) so it fits entirely on the GPU.
# Switching to bfloat16 is natively supported by Hopper (H200) and highly recommended.
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16, # <-- Optimized for H200
    bnb_4bit_use_double_quant=True,        
    bnb_4bit_quant_type="nf4",
    llm_int8_enable_fp32_cpu_offload=True  # <-- Kept for safety if KV cache slightly exceeds 141GB
)

# Load model safely
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    quantization_config=bnb_config,
    torch_dtype=torch.bfloat16,            # <-- Optimized for H200
    trust_remote_code=True
)

print("Fine-tuned model loaded successfully! Generating text...")

# --- PROMPT & GENERATION ---
# Define the system prompt and the user's question
messages = [
    {"role": "system", "content": "You are LuminoLex-Aura an AI model developed by VERBAREX."},
    {"role": "user", "content": "Greetings! Could you introduce yourself, tell me who created you, and explain what kind of advanced tasks you are capable of handling?"}
]

# Apply the model's specific chat format
input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

# Generate the response
outputs = model.generate(
    input_ids,
    max_new_tokens=256,  # Adjusted to allow for a detailed introduction
    do_sample=True,
    temperature=0.7, 
    top_p=0.95
)

# Slice off the input prompt so we only print the model's new response
input_length = input_ids.shape[1]
generated_tokens = outputs[0][input_length:]

print("\n--- OUTPUT ---")
print(tokenizer.decode(generated_tokens, skip_special_tokens=True))