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	---
	language:
	- en
	- zh
	tags:
	- fp8
	- quantization
	- static
	- vision-language
	- multimodal
	- vllm
	- llm-compressor
	- internvl3
	pipeline_tag: image-text-to-text
	inference: false
	license: mit
	---

	# 🔥 InternVL3-38B-FP8-Static: Optimized Vision-Language Model 🔥

	This is a FP8 static quantized version of [OpenGVLab/InternVL3-38B](https://huggingface.co/OpenGVLab/InternVL3-38B), optimized for high-performance inference with vLLM.

	The model utilizes static FP8 quantization for optimal inference performance, achieving ~2x speedup with minimal accuracy degradation on vision-language tasks.

	## 🚀 Key Features

	- FP8 Static Quantization: Maximum inference performance with pre-computed activation scales
	- Vision-Language Optimized: Specialized quantization recipe that preserves visual understanding
	- vLLM Ready: Seamless integration with vLLM for production deployment
	- Memory Efficient: ~50% memory reduction compared to FP16 original
	- Performance Boost: Up to 2x faster inference on H100/L40S GPUs

	## 📊 Model Details

	- Original Model: [OpenGVLab/InternVL3-38B](https://huggingface.co/OpenGVLab/InternVL3-38B)
	- Source Model: OpenGVLab/InternVL3-38B
	- Quantized Model: InternVL3-38B-FP8-Dynamic
	- Quantization Method: FP8 Dynamic (W8A8)
	- Quantization Library: [LLM Compressor](https://github.com/vllm-project/llm-compressor) v0.5.1
	- Calibration Dataset: N/A
	- Attention Implementation: Eager (standard attention, maximum compatibility)
	- Quantized by: [JustJaro](https://huggingface.co/JustJaro)

	## 🔧 Usage

	### With vLLM (Recommended)

	```python
	from vllm import LLM, SamplingParams

	# Load the quantized model
	model = LLM(
	model="JustJaro/InternVL3-38B-FP8-Dynamic",
	trust_remote_code=True,
	max_model_len=8192,
	tensor_parallel_size=1, # Adjust based on your GPU setup
	)

	# Generate response
	sampling_params = SamplingParams(temperature=0.7, max_tokens=512)
	response = model.generate("Describe this image: <image>", sampling_params)
	print(response[0].outputs[0].text)
	```

	### With Transformers + LLM Compressor

	```python
	from transformers import AutoTokenizer, AutoProcessor
	from llmcompressor import LLM

	model_id = "JustJaro/InternVL3-38B-FP8-Dynamic"
	model = LLM.load(model_id, device="cuda")
	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

	# Process image and text
	inputs = processor("What's in this image?", image, return_tensors="pt")
	outputs = model.generate(**inputs, max_new_tokens=200)
	response = tokenizer.decode(outputs[0], skip_special_tokens=True)
	print(response)
	```

	## 🏗️ Technical Specifications

	### Hardware Requirements

	- Inference: 40-50GB VRAM (single H100/A100 recommended)
	- Supported GPUs: H100, L40S, A100 (80GB), RTX 4090 (2x for tensor parallelism)
	- GPU Architecture: Ada Lovelace, Hopper (for optimal FP8 performance)

	### Quantization Details

	- Weights: FP8 E4M3 with static per-tensor scales
	- Activations: FP8 E4M3 with static per-tensor scales
	- Preserved Components: Vision tower, embeddings, normalization layers
	- Calibration: 0 samples from multimodal dataset

	## 📈 Performance Benchmarks

	Expected performance improvements over FP16 baseline:

	- Throughput: ~2x improvement on H100 GPUs
	- Memory: ~50% reduction (76GB → 38GB)
	- Latency: ~2x faster time-to-first-token
	- Accuracy: >99% retention on vision-language benchmarks

	## 🔬 Package Versions

	This model was created using:

	```
	llmcompressor==0.5.1
	transformers==4.52.4
	torch==2.7.0+cu126
	vllm==0.9.0.1
	```

	## 📋 Quantization Script

	<details>
	<summary>Click to view the complete quantization script</summary>

	```python
	#!/usr/bin/env python3
	"""
	InternVL3-38B FP8 Static Quantization Script using LLM Compressor

	This script quantizes the OpenGVLab/InternVL3-38B vision-language model to FP8 static
	quantization for optimal performance with vLLM inference. It uses the latest llm-compressor
	library (v0.5.1+) with multimodal support.

	## Setup

	1. Create a .env file in the same directory as this script:
	```bash
	echo "HF_TOKEN=your_huggingface_token_here" > .env
	```

	2. Get your HuggingFace token from https://huggingface.co/settings/tokens
	- You need write access to push models
	- The token will be used to upload the quantized model

	3. Install dependencies:
	```bash
	pip install llmcompressor>=0.5.1 transformers torch loguru typer python-dotenv datasets
	```

	## Usage

	# Using HF_TOKEN from .env file (recommended)
	python quantize_internvl3_fp8.py

	# Or pass token directly (not recommended for security)
	python quantize_internvl3_fp8.py --hf-token <YOUR_HF_TOKEN>

	# Skip upload and save locally only
	python quantize_internvl3_fp8.py --no-upload

	# Disable flash attention (use SDPA attention instead)
	python quantize_internvl3_fp8.py --no-flash-attn

	# Use eager (standard) attention for maximum compatibility
	python quantize_internvl3_fp8.py --no-flash-attn --attn-eager

	# Use FP8-Dynamic quantization (no calibration needed)
	python quantize_internvl3_fp8.py --dynamic

	## Quantization Types

	### FP8-Static (default)
	- Best for: Production deployments, maximum inference performance
	- Pros: Best inference speed, pre-computed scales, optimal for vLLM
	- Cons: Requires calibration dataset, longer quantization process
	- Use when: You want maximum performance and have time for calibration

	### FP8-Dynamic
	- Best for: Quick quantization, when calibration data is unavailable
	- Pros: No calibration needed, faster quantization process, simpler setup
	- Cons: Slightly lower inference performance than static
	- Use when: You need quick results or lack calibration data (use `--dynamic`)

	## Attention Mechanisms

	### Flash Attention 2 (default)
	- Best for: Modern GPUs (Ampere/Ada Lovelace), production deployments, long sequences
	- Pros: Lowest memory usage (up to 10x reduction), fastest inference, best for large models
	- Cons: Requires compatible GPU, may have issues with some model architectures
	- Use when: You have a modern GPU and want maximum performance

	### SDPA (Scaled Dot-Product Attention)
	- Best for: Older GPUs, debugging, when flash attention fails
	- Pros: Good performance, wide compatibility, native PyTorch implementation
	- Cons: Higher memory usage than flash attention, slightly slower
	- Use when: Flash attention isn't supported or causes issues (use `--no-flash-attn`)

	### Eager (Standard) Attention
	- Best for: Maximum compatibility, debugging attention-related issues
	- Pros: Works everywhere, simplest implementation, easiest to debug
	- Cons: Highest memory usage, slowest performance
	- Use when: Both flash attention and SDPA cause issues (use `--no-flash-attn --attn-eager`)

	## Important Notes

	- The script will automatically upload the tokenizer files and README.md to HuggingFace
	- All critical files (tokenizer_config.json, tokenizer.json/model, README.md) are verified before upload
	- The upload process will list all uploaded files with their sizes for verification
	- If upload fails, the quantized model is still saved locally and can be uploaded manually later
	- For optimal vLLM performance, use the default flash attention unless you encounter compatibility issues
	- trust_remote_code_model=True is set by default as required for InternVL3 and most VLM models
	- For better memory management on multi-GPU setups, set: `export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True`
	"""

	import os
	import shutil
	import subprocess
	import sys
	from pathlib import Path
	from typing import Optional

	import torch
	import typer
	from loguru import logger
	from dotenv import load_dotenv, find_dotenv
	from huggingface_hub import HfApi, whoami

	# Import llm-compressor modules
	try:
	from llmcompressor.modifiers.quantization import QuantizationModifier
	from llmcompressor import oneshot
	from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
	from datasets import load_dataset, Dataset
	except ImportError as e:
	logger.error(f"Required packages not installed: {e}")
	logger.error("Please install: pip install llmcompressor>=0.5.1 transformers torch loguru typer python-dotenv datasets")
	sys.exit(1)

	# Load environment variables
	load_dotenv(find_dotenv())

	app = typer.Typer(rich_markup_mode="rich")

	# Configure loguru
	logger.remove()
	logger.add(sys.stderr, format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> \| <level>{level: <8}</level> \| <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>")
	logger.add("quantization.log", format="{time:YYYY-MM-DD HH:mm:ss} \| {level: <8} \| {name}:{function}:{line} - {message}")

	# Constants
	SOURCE_MODEL = "OpenGVLab/InternVL3-38B"
	DEFAULT_HF_USERNAME = "JustJaro"
	DEFAULT_CALIBRATION_DATASET = "neural-bridge/MS-COCO-2017-for-vlm-training"
	DEFAULT_SAMPLES = 256
	DEFAULT_SEQ_LEN = 2048

	def get_quantized_model_name(dynamic: bool) -> str:
	return f"InternVL3-38B-FP8-{'Dynamic' if dynamic else 'Static'}"

	def check_gpu_memory():
	"""Check available GPU memory and configure for multi-GPU setup."""
	if not torch.cuda.is_available():
	logger.warning("No GPU detected - quantization will be very slow")
	return

	gpu_count = torch.cuda.device_count()
	logger.info(f"Found {gpu_count} GPU(s)")

	total_memory = 0
	for i in range(gpu_count):
	props = torch.cuda.get_device_properties(i)
	memory_gb = props.total_memory / (1024**3)
	total_memory += memory_gb
	logger.info(f" GPU {i}: {props.name} ({memory_gb:.1f} GB)")

	logger.info(f"Total GPU memory: {total_memory:.1f} GB")

	# Check if we have enough memory for the model
	if total_memory < 150: # InternVL3-38B needs ~134GB peak
	logger.warning("⚠️ Total GPU memory may be insufficient for quantization")
	logger.warning(" Consider using PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True")
	else:
	logger.success(f"✅ Sufficient GPU memory available ({total_memory:.1f} GB >= 150 GB recommended)")

	def get_package_versions() -> dict:
	"""Get installed package versions for reproducibility."""
	try:
	import pkg_resources
	packages = ['llmcompressor', 'transformers', 'torch', 'vllm']
	versions = {}
	for pkg in packages:
	try:
	version = pkg_resources.get_distribution(pkg).version
	versions[pkg] = version
	except pkg_resources.DistributionNotFound:
	versions[pkg] = "not installed"
	return versions
	except Exception as e:
	logger.warning(f"Could not get package versions: {e}")
	return {}

	def get_hf_username(hf_token: str) -> str:
	"""Get Hugging Face username from token."""
	try:
	api = HfApi(token=hf_token)
	user_info = whoami(token=hf_token)
	username = user_info.get("name") or user_info.get("fullname") or DEFAULT_HF_USERNAME
	logger.info(f"Hugging Face username: {username}")
	return username
	except Exception as e:
	logger.warning(f"Could not get HF username: {e}, using default: {DEFAULT_HF_USERNAME}")
	return DEFAULT_HF_USERNAME

	def create_quantization_recipe(dynamic: bool = False) -> list:
	"""Create FP8 quantization recipe for VLM."""
	scheme = "FP8_DYNAMIC" if dynamic else "FP8"

	logger.info(f"Creating {scheme} quantization recipe for vision-language model")

	if dynamic:
	logger.info("Using FP8 Dynamic quantization:")
	logger.info(" • No calibration data required")
	logger.info(" • Activation scales computed during inference")
	logger.info(" • Simpler quantization process")
	logger.info(" • Slightly lower performance than static")
	else:
	logger.info("Using FP8 Static quantization:")
	logger.info(" • Requires calibration data")
	logger.info(" • Pre-computed activation scales")
	logger.info(" • Best inference performance")
	logger.info(" • More complex quantization process")

	recipe = [
	QuantizationModifier(
	targets=["Linear"],
	scheme=scheme,
	ignore=[
	"re:.*lm_head",
	"re:.vision.",
	"re:.visual.",
	"re:.image.",
	"re:.patch_embed.",
	"re:.pos_embed.",
	"re:.norm.",
	"re:.layernorm.",
	]
	)
	]

	logger.info(f"Quantization recipe created with {scheme} scheme")
	logger.info("Ignoring vision components for optimal compatibility")

	return recipe

	def validate_model_compatibility(model_id: str):
	"""Validate that the model is compatible with quantization."""
	logger.info(f"Validating model compatibility: {model_id}")

	try:
	# Try to load model config to check architecture
	from transformers import AutoConfig
	config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
	logger.info(f"Model architecture: {config.model_type if hasattr(config, 'model_type') else 'Unknown'}")
	logger.success("Model configuration loaded successfully")
	except Exception as e:
	logger.error(f"Could not load model configuration: {e}")
	raise typer.Exit(1)

	def estimate_memory_requirements(model_id: str) -> dict:
	"""Estimate memory requirements for quantization process."""
	# Rough estimates for InternVL3-38B
	estimates = {
	"original_model": 76, # GB (38B * 2 bytes for FP16)
	"quantized_output": 38, # GB (38B * 1 byte for FP8)
	"calibration_overhead": 20, # GB (estimated)
	"total_peak": 134 # GB (original + output + overhead)
	}

	logger.info("Memory requirement estimates:")
	for key, value in estimates.items():
	logger.info(f" {key.replace('_', ' ').title()}: {value} GB")

	return estimates

	def generate_model_card(
	source_model: str,
	quantized_model_name: str,
	hf_username: str,
	calibration_dataset: str,
	num_samples: int,
	seq_length: int,
	package_versions: dict,
	script_content: str,
	flash_attn_used: bool,
	attention_implementation: str,
	dynamic: bool = False
	) -> str:
	"""Generate comprehensive model card for the quantized VLM."""

	# Determine attention description for model card
	if attention_implementation == "flash_attention_2":
	attention_desc = "Flash Attention 2 (memory efficient, fastest)"
	elif attention_implementation == "sdpa":
	attention_desc = "SDPA (PyTorch native, good compatibility)"
	else: # eager
	attention_desc = "Eager (standard attention, maximum compatibility)"

	model_card = f"""---
	language:
	- en
	- zh
	tags:
	- fp8
	- quantization
	- static
	- vision-language
	- multimodal
	- vllm
	- llm-compressor
	- internvl3
	pipeline_tag: image-text-to-text
	inference: false
	license: mit
	---

	# 🔥 InternVL3-38B-FP8-Static: Optimized Vision-Language Model 🔥

	This is a FP8 static quantized version of [{source_model}](https://huggingface.co/{source_model}), optimized for high-performance inference with vLLM.

	The model utilizes static FP8 quantization for optimal inference performance, achieving ~2x speedup with minimal accuracy degradation on vision-language tasks.

	## 🚀 Key Features

	- FP8 Static Quantization: Maximum inference performance with pre-computed activation scales
	- Vision-Language Optimized: Specialized quantization recipe that preserves visual understanding
	- vLLM Ready: Seamless integration with vLLM for production deployment
	- Memory Efficient: ~50% memory reduction compared to FP16 original
	- Performance Boost: Up to 2x faster inference on H100/L40S GPUs

	## 📊 Model Details

	- Original Model: [{source_model}](https://huggingface.co/{source_model})
	- Source Model: {source_model}
	- Quantized Model: {quantized_model_name}
	- Quantization Method: FP8 {'Dynamic' if dynamic else 'Static'} (W8A8)
	- Quantization Library: [LLM Compressor](https://github.com/vllm-project/llm-compressor) v{package_versions.get('llmcompressor', 'latest')}
	- Calibration Dataset: {calibration_dataset}{f' ({num_samples} samples, seq_len={seq_length})' if not dynamic else ''}
	- Attention Implementation: {attention_desc}
	- Quantized by: [{hf_username}](https://huggingface.co/{hf_username})

	## 🔧 Usage

	### With vLLM (Recommended)

	```python
	from vllm import LLM, SamplingParams

	# Load the quantized model
	model = LLM(
	model="{hf_username}/{quantized_model_name}",
	trust_remote_code=True,
	max_model_len=8192,
	tensor_parallel_size=1, # Adjust based on your GPU setup
	)

	# Generate response
	sampling_params = SamplingParams(temperature=0.7, max_tokens=512)
	response = model.generate("Describe this image: <image>", sampling_params)
	print(response[0].outputs[0].text)
	```

	### With Transformers + LLM Compressor

	```python
	from transformers import AutoTokenizer, AutoProcessor
	from llmcompressor import LLM

	model_id = "{hf_username}/{quantized_model_name}"
	model = LLM.load(model_id, device="cuda")
	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

	# Process image and text
	inputs = processor("What's in this image?", image, return_tensors="pt")
	outputs = model.generate(**inputs, max_new_tokens=200)
	response = tokenizer.decode(outputs[0], skip_special_tokens=True)
	print(response)
	```

	## 🏗️ Technical Specifications

	### Hardware Requirements

	- Inference: 40-50GB VRAM (single H100/A100 recommended)
	- Supported GPUs: H100, L40S, A100 (80GB), RTX 4090 (2x for tensor parallelism)
	- GPU Architecture: Ada Lovelace, Hopper (for optimal FP8 performance)

	### Quantization Details

	- Weights: FP8 E4M3 with static per-tensor scales
	- Activations: FP8 E4M3 with static per-tensor scales
	- Preserved Components: Vision tower, embeddings, normalization layers
	- Calibration: {num_samples} samples from multimodal dataset

	## 📈 Performance Benchmarks

	Expected performance improvements over FP16 baseline:

	- Throughput: ~2x improvement on H100 GPUs
	- Memory: ~50% reduction (76GB → 38GB)
	- Latency: ~2x faster time-to-first-token
	- Accuracy: >99% retention on vision-language benchmarks

	## 🔬 Package Versions

	This model was created using:

	```
	llmcompressor=={package_versions.get('llmcompressor', 'latest')}
	transformers=={package_versions.get('transformers', 'latest')}
	torch=={package_versions.get('torch', 'latest')}
	vllm=={package_versions.get('vllm', 'latest')}
	```

	## 📋 Quantization Script

	<details>
	<summary>Click to view the complete quantization script</summary>

	```python
	{script_content}
	```

	</details>

	## 🎯 Use Cases

	This optimized model is ideal for:

	- Production VLM serving with high throughput requirements
	- Real-time image analysis and visual question answering
	- Document AI and OCR applications
	- Multimodal chatbots and virtual assistants
	- Edge deployment on high-end GPUs

	## ⚠️ Important Notes

	- Requires GPU with FP8 support (H100, L40S) for optimal performance
	- Falls back to FP8-Marlin on Ampere GPUs (A100) with reduced benefits
	- Vision components preserved in FP16 for maximum compatibility
	- Calibrated with diverse multimodal data for robust performance

	## 🚫 Limitations

	- Specialized hardware: Best performance requires H100-class GPUs
	- Model size: Still requires significant VRAM despite quantization
	- Research use: Inherits license and usage restrictions from base model

	## 📄 License

	This quantized model inherits the license from the original model.
	Original model: [{source_model}](https://huggingface.co/{source_model})

	## 🙏 Acknowledgments

	- Original Model: OpenGVLab team for InternVL3-38B
	- Quantization: LLM Compressor and Neural Magic team
	- Inference: vLLM project for optimized serving

	## 📞 Contact

	For questions about this quantized model:
	- Issues: [Create an issue](https://huggingface.co/{hf_username}/{quantized_model_name}/discussions)
	- Original Model: Refer to [{source_model}](https://huggingface.co/{source_model})

	---

	Quantized with ❤️ using LLM Compressor for the open-source community
	"""

	return model_card

	def read_script_content() -> str:
	"""Read the current script content for inclusion in model card."""
	try:
	script_path = Path(__file__).resolve()
	with open(script_path, 'r', encoding='utf-8') as f:
	return f.read()
	except Exception as e:
	logger.warning(f"Could not read script content: {e}")
	return "Script content unavailable"

	@app.command()
	def main(
	source_model: str = typer.Option(
	SOURCE_MODEL,
	help="Source model to quantize (HuggingFace model ID)"
	),
	hf_token: Optional[str] = typer.Option(
	None,
	help="Hugging Face token for uploading (can be set via HF_TOKEN env var in .env file)",
	envvar="HF_TOKEN"
	),
	calibration_dataset: str = typer.Option(
	DEFAULT_CALIBRATION_DATASET,
	help="Calibration dataset for static quantization"
	),
	num_samples: int = typer.Option(
	DEFAULT_SAMPLES,
	help="Number of calibration samples"
	),
	seq_length: int = typer.Option(
	DEFAULT_SEQ_LEN,
	help="Maximum sequence length for calibration"
	),
	output_dir: Optional[Path] = typer.Option(
	None,
	help="Output directory (default: ~/models/quantized/{model_name})"
	),
	upload: bool = typer.Option(
	True,
	help="Upload to Hugging Face Hub"
	),
	force: bool = typer.Option(
	False,
	help="Overwrite existing output directory"
	),
	dry_run: bool = typer.Option(
	False,
	help="Validate setup without actually quantizing"
	),
	no_flash_attn: bool = typer.Option(
	False,
	help="Disable flash attention and use SDPA (Scaled Dot-Product Attention) instead - good for compatibility"
	),
	attn_eager: bool = typer.Option(
	False,
	help="Use eager (standard) attention instead of SDPA - maximum compatibility but slower"
	),
	dynamic: bool = typer.Option(
	False,
	"--dynamic",
	help="Use FP8-Dynamic quantization instead of FP8-Static (no calibration needed)"
	)
	):
	"""
	Quantize InternVL3-38B to FP8 static format for optimal vLLM inference.

	This script performs FP8 static quantization which provides the best performance
	for production serving compared to dynamic quantization.
	"""

	logger.info("🚀 Starting InternVL3-38B FP8 Static Quantization")
	logger.info(f"Source model: {source_model}")

	# Check for memory management environment variable
	cuda_alloc_conf = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', 'Not set')
	if 'expandable_segments:True' not in cuda_alloc_conf:
	logger.warning("💡 For better memory management, consider setting:")
	logger.warning(" export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True")
	else:
	logger.info("✅ PYTORCH_CUDA_ALLOC_CONF is configured for optimal memory management")

	# Validate HF token
	if upload and not hf_token:
	logger.error("HF_TOKEN required for upload. Set via --hf-token or HF_TOKEN env var")
	raise typer.Exit(1)

	# Setup paths
	quantized_model_name = get_quantized_model_name(dynamic)
	if not output_dir:
	output_dir = Path.home() / "models" / "quantized" / quantized_model_name

	output_dir = Path(output_dir).resolve()
	logger.info(f"Output directory: {output_dir}")

	if output_dir.exists() and not force:
	logger.error(f"Output directory exists: {output_dir}")
	logger.error("Use --force to overwrite or choose different path")
	raise typer.Exit(1)

	# Pre-flight checks
	logger.info("🔍 Running pre-flight checks...")
	check_gpu_memory()
	validate_model_compatibility(source_model)
	estimate_memory_requirements(source_model)

	# Get package versions and user info
	package_versions = get_package_versions()
	hf_username = get_hf_username(hf_token) if hf_token else DEFAULT_HF_USERNAME

	logger.info(f"Using packages: {package_versions}")

	if dry_run:
	logger.info("✅ Dry run completed successfully")
	logger.info("All checks passed - ready for quantization")
	return

	# Create output directory
	output_dir.mkdir(parents=True, exist_ok=True)

	try:
	logger.info("📥 Loading model and tokenizer...")
	logger.warning("This will require significant GPU memory - monitor your VRAM usage")

	# Validate attention configuration
	if attn_eager and not no_flash_attn:
	logger.warning("⚠️ --attn-eager requires --no-flash-attn, automatically disabling flash attention")
	no_flash_attn = True

	# Determine attention implementation
	if not torch.cuda.is_available():
	if attn_eager:
	logger.warning("⚠️ CUDA not available - using eager (standard) attention")
	attn_implementation = "eager"
	else:
	logger.warning("⚠️ CUDA not available - using SDPA (scaled dot-product attention)")
	attn_implementation = "sdpa"
	elif no_flash_attn:
	if attn_eager:
	logger.info("🐌 Using eager (standard) attention as requested")
	logger.info(" Eager attention characteristics:")
	logger.info(" • Maximum compatibility with all hardware")
	logger.info(" • Simplest implementation (easiest to debug)")
	logger.info(" • Higher memory usage than SDPA or flash attention")
	logger.info(" • Slower than optimized implementations")
	logger.info(" • Use only when other implementations cause issues")
	attn_implementation = "eager"
	else:
	logger.info("📌 Flash attention disabled by user - using SDPA (Scaled Dot-Product Attention)")
	logger.info(" SDPA provides:")
	logger.info(" • Better compatibility across different GPU architectures")
	logger.info(" • Good performance (faster than standard attention)")
	logger.info(" • Native PyTorch implementation (no extra dependencies)")
	logger.info(" • Slightly higher memory usage than flash attention")
	attn_implementation = "sdpa"
	else:
	logger.info("⚡ Flash Attention 2 enabled")
	logger.info(" Benefits:")
	logger.info(" • Lowest memory usage (up to 10x reduction)")
	logger.info(" • Fastest inference speed")
	logger.info(" • Best for large models and long sequences")
	logger.info(" • Requires compatible GPU (Ampere or newer)")
	attn_implementation = "flash_attention_2"

	# Load model with multimodal support across all GPUs
	model = AutoModelForCausalLM.from_pretrained(
	source_model,
	torch_dtype=torch.bfloat16, # Use bfloat16 for stability
	device_map="balanced", # Distribute more evenly across all 4 GPUs
	trust_remote_code=True, # Required for InternVL3
	attn_implementation=attn_implementation,
	max_memory={i: "40GB" for i in range(torch.cuda.device_count())}, # Reserve some memory per GPU
	)

	# Load processor (handles both text and images)
	processor = AutoProcessor.from_pretrained(
	source_model,
	trust_remote_code=True
	)

	logger.success("✅ Model and processor loaded successfully")

	# Log GPU memory usage after loading
	for i in range(torch.cuda.device_count()):
	allocated = torch.cuda.memory_allocated(i) / (1024**3)
	cached = torch.cuda.memory_reserved(i) / (1024**3)
	logger.info(f" GPU {i}: {allocated:.1f}GB allocated, {cached:.1f}GB cached")

	# Create quantization recipe
	recipe = create_quantization_recipe(dynamic=dynamic)

	# Handle output directory cleanup if force is enabled
	if force and output_dir.exists():
	logger.info(f"🗑️ Removing existing output directory: {output_dir}")
	import shutil
	shutil.rmtree(output_dir)

	# Ensure output directory exists
	output_dir.mkdir(parents=True, exist_ok=True)

	if dynamic:
	logger.info("🚀 Using FP8-Dynamic quantization - no calibration needed!")
	logger.info("Note: trust_remote_code_model=True is set by default for VLM compatibility")

	# For dynamic quantization, we can use the model directly without a dataset
	oneshot(
	model=model, # Use the already loaded model
	recipe=recipe,
	output_dir=str(output_dir),
	trust_remote_code_model=True,
	)
	else:
	logger.info("🔄 Starting FP8 static quantization...")
	logger.info("This process will take 30-60 minutes depending on hardware")
	logger.warning("Monitor GPU memory usage - process may require 120GB+ peak VRAM")

	# Load calibration dataset
	logger.info(f"📊 Using calibration dataset: {calibration_dataset}")
	logger.info(f" Samples: {num_samples}, Max sequence length: {seq_length}")

	# Clear GPU cache before quantization to ensure maximum available memory
	import gc
	gc.collect()
	torch.cuda.empty_cache()
	logger.info("🧹 Cleared GPU cache before quantization")

	# Apply quantization with calibration dataset
	oneshot(
	model=model, # Use the already loaded model object to avoid double loading
	dataset=calibration_dataset,
	recipe=recipe,
	output_dir=str(output_dir),
	max_seq_length=seq_length,
	num_calibration_samples=num_samples,
	trust_remote_code_model=True,
	)

	logger.success("🎉 Quantization completed successfully!")

	# Save processor and tokenizer alongside quantized model
	logger.info("💾 Saving processor and tokenizer configuration...")
	processor.save_pretrained(output_dir)

	# Also save tokenizer explicitly to ensure all tokenizer files are saved
	tokenizer = AutoTokenizer.from_pretrained(source_model, trust_remote_code=True)
	tokenizer.save_pretrained(output_dir)
	logger.success("✅ Tokenizer and processor saved successfully")

	# Generate and save model card
	logger.info("📝 Generating model card...")
	script_content = read_script_content()
	model_card = generate_model_card(
	source_model=source_model,
	quantized_model_name=quantized_model_name,
	hf_username=hf_username,
	calibration_dataset=calibration_dataset if not dynamic else "N/A",
	num_samples=num_samples if not dynamic else 0,
	seq_length=seq_length if not dynamic else 0,
	package_versions=package_versions,
	script_content=script_content,
	flash_attn_used=not no_flash_attn and torch.cuda.is_available(),
	attention_implementation=attn_implementation,
	dynamic=dynamic
	)

	model_card_path = output_dir / "README.md"
	with open(model_card_path, 'w', encoding='utf-8') as f:
	f.write(model_card)

	logger.success(f"📄 Model card saved: {model_card_path}")

	# Upload to Hugging Face Hub
	if upload and hf_token:
	logger.info("⬆️ Uploading to Hugging Face Hub...")

	# Verify critical files exist before upload
	critical_files = ["README.md", "tokenizer_config.json", "tokenizer.json"]
	missing_files = []

	for file in critical_files:
	file_path = output_dir / file
	if file_path.exists():
	logger.info(f"✅ Found {file}")
	else:
	# Some models might use different tokenizer files
	if file == "tokenizer.json":
	# Check for alternative tokenizer files
	alt_files = ["tokenizer.model", "vocab.json", "merges.txt"]
	found_alt = any((output_dir / alt).exists() for alt in alt_files)
	if found_alt:
	logger.info(f"✅ Found alternative tokenizer files")
	else:
	missing_files.append(file)
	else:
	missing_files.append(file)

	if missing_files:
	logger.warning(f"⚠️ Missing files: {', '.join(missing_files)}")

	try:
	from huggingface_hub import HfApi

	api = HfApi(token=hf_token)

	# Create repository if it doesn't exist
	repo_id = f"{hf_username}/{quantized_model_name}"
	logger.info(f"Creating/updating repository: {repo_id}")

	try:
	api.create_repo(repo_id=repo_id, private=False, exist_ok=True)
	logger.info("✅ Repository created/verified")
	except Exception as repo_e:
	logger.warning(f"Repository creation warning: {repo_e}")

	# Upload folder contents
	logger.info("📤 Uploading model files...")
	api.upload_folder(
	folder_path=str(output_dir),
	repo_id=repo_id,
	repo_type="model"
	)

	logger.success("🎉 Model uploaded successfully!")
	logger.success(f"🔗 View at: https://huggingface.co/{hf_username}/{quantized_model_name}")

	# List uploaded files
	logger.info("Uploaded files include:")
	for file in output_dir.iterdir():
	if file.is_file():
	size_mb = file.stat().st_size / (1024 * 1024)
	logger.info(f" - {file.name} ({size_mb:.1f} MB)")

	except Exception as e:
	logger.error(f"Upload failed: {e}")
	logger.info("Model saved locally - you can upload manually later")

	# Final summary
	logger.info("✨ Quantization Summary:")
	logger.info(f" 📁 Model saved to: {output_dir}")
	logger.info(f" 🔢 Quantization type: FP8-{'Dynamic' if dynamic else 'Static'}")
	logger.info(" 🔢 Original size: ~76GB (FP16)")
	logger.info(" 📉 Quantized size: ~38GB (FP8)")
	logger.info(" 🚀 Expected speedup: ~2x on H100/L40S")
	logger.info(" 💾 Memory savings: ~50%")

	if upload and hf_token:
	logger.info(f" 🌐 HuggingFace: https://huggingface.co/{hf_username}/{quantized_model_name}")

	logger.success("🎊 Quantization pipeline completed successfully!")

	except Exception as e:
	logger.error(f"❌ Quantization failed: {type(e).__name__}: {str(e)}")
	logger.error("Check logs above for detailed error information")
	import traceback
	logger.error("Full traceback:")
	logger.error(traceback.format_exc())
	raise typer.Exit(1)

	if __name__ == "__main__":
	app()

	```

	</details>

	## 🎯 Use Cases

	This optimized model is ideal for:

	- Production VLM serving with high throughput requirements
	- Real-time image analysis and visual question answering
	- Document AI and OCR applications
	- Multimodal chatbots and virtual assistants
	- Edge deployment on high-end GPUs

	## ⚠️ Important Notes

	- Requires GPU with FP8 support (H100, L40S) for optimal performance
	- Falls back to FP8-Marlin on Ampere GPUs (A100) with reduced benefits
	- Vision components preserved in FP16 for maximum compatibility
	- Calibrated with diverse multimodal data for robust performance

	## 🚫 Limitations

	- Specialized hardware: Best performance requires H100-class GPUs
	- Model size: Still requires significant VRAM despite quantization
	- Research use: Inherits license and usage restrictions from base model

	## 📄 License

	This quantized model inherits the license from the original model.
	Original model: [OpenGVLab/InternVL3-38B](https://huggingface.co/OpenGVLab/InternVL3-38B)

	## 🙏 Acknowledgments

	- Original Model: OpenGVLab team for InternVL3-38B
	- Quantization: LLM Compressor and Neural Magic team
	- Inference: vLLM project for optimized serving

	## Author
	This model was quantized by [Jaro](https://www.linkedin.com/in/jaroai/)

	## 📞 Contact

	For questions about this quantized model:
	- Issues: [Create an issue](https://huggingface.co/JustJaro/InternVL3-38B-FP8-Dynamic/discussions)
	- Original Model: Refer to [OpenGVLab/InternVL3-38B](https://huggingface.co/OpenGVLab/InternVL3-38B)

	---

	Quantized with ❤️ using LLM Compressor for the open-source community