Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/orchestra-research/ai-research-skills --skill awq-quantizationAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
AWQ (Activation-aware Weight Quantization)
4-bit quantization that preserves salient weights based on activation patterns, achieving 3x speedup with minimal accuracy loss.
When to use AWQ
Use AWQ when:
- Need 4-bit quantization with <5% accuracy loss
- Deploying instruction-tuned or chat models (AWQ generalizes better)
- Want ~2.5-3x inference speedup over FP16
- Using vLLM for production serving
- Have Ampere+ GPUs (A100, H100, RTX 40xx) for Marlin kernel support
Use GPTQ instead when:
- Need maximum ecosystem compatibility (more tools support GPTQ)
- Working with ExLlamaV2 backend specifically
- Have older GPUs without Marlin support
Use bitsandbytes instead when:
- Need zero calibration overhead (quantize on-the-fly)
- Want to fine-tune with QLoRA
- Prefer simpler integration
Quick start
Installation
bash
# Default (Triton kernels)
pip install autoawq
# With optimized CUDA kernels + Flash Attention
pip install autoawq[kernels]
# Intel CPU/XPU optimization
pip install autoawq[cpu]Requirements: Python 3.8+, CUDA 11.8+, Compute Capability 7.5+
Load pre-quantized model
python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_name = "TheBloke/Mistral-7B-Instruct-v0.2-AWQ"
model = AutoAWQForCausalLM.from_quantized(
model_name,
fuse_layers=True # Enable fused attention for speed
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Generate
inputs = tokenizer("Explain quantum computing", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Quantize your own model
python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_path = "mistralai/Mistral-7B-Instruct-v0.2"
# Load model and tokenizer
model = AutoAWQForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
# Quantization config
quant_config = {
"zero_point": True, # Use zero-point quantization
"q_group_size": 128, # Group size (128 recommended)
"w_bit": 4, # 4-bit weights
"version": "GEMM" # GEMM for batch, GEMV for single-token
}
# Quantize (uses pileval dataset by default)
model.quantize(tokenizer, quant_config=quant_config)
# Save
model.save_quantized("mistral-7b-awq")
tokenizer.save_pretrained("mistral-7b-awq")Timing: ~10-15 min for 7B, ~1 hour for 70B models.
AWQ vs GPTQ vs bitsandbytes
| Feature | AWQ | GPTQ | bitsandbytes |
|---|---|---|---|
| Speedup (4-bit) | ~2.5-3x | ~2x | ~1.5x |
| Accuracy loss | <5% | ~5-10% | ~5-15% |
| Calibration | Minimal (128-1K tokens) | More extensive | None |
| Overfitting risk | Low | Higher | N/A |
| Best for | Production inference | GPU inference | Easy integration |
| vLLM support | Native | Yes | Limited |
Key insight: AWQ assumes not all weights are equally important. It protects ~1% of salient weights identified by activation patterns, reducing quantization error without mixed-precision overhead.
Kernel backends
GEMM (default, batch inference)
python
quant_config = {
"zero_point": True,
"q_group_size": 128,
"w_bit": 4,
"version": "GEMM" # Best for batch sizes > 1
}GEMV (single-token generation)
python
quant_config = {
"version": "GEMV" # 20% faster for batch_size=1
}Limitation: Only batch size 1, not good for large context.
Marlin (Ampere+ GPUs)
python
from transformers import AwqConfig, AutoModelForCausalLM
config = AwqConfig(
bits=4,
version="marlin" # 2x faster on A100/H100
)
model = AutoModelForCausalLM.from_pretrained(
"TheBloke/Mistral-7B-AWQ",
quantization_config=config
)Requirements: Compute Capability 8.0+ (A100, H100, RTX 40xx)
ExLlamaV2 (AMD compatible)
python
config = AwqConfig(
bits=4,
version="exllama" # Faster prefill, AMD GPU support
)HuggingFace Transformers integration
Direct loading
python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"TheBloke/zephyr-7B-alpha-AWQ",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("TheBloke/zephyr-7B-alpha-AWQ")Fused modules (recommended)
python
from transformers import AwqConfig, AutoModelForCausalLM
config = AwqConfig(
bits=4,
fuse_max_seq_len=512, # Max sequence length for fusing
do_fuse=True # Enable fused attention/MLP
)
model = AutoModelForCausalLM.from_pretrained(
"TheBloke/Mistral-7B-OpenOrca-AWQ",
quantization_config=config
)Note: Fused modules cannot combine with FlashAttention2.
vLLM integration
python
from vllm import LLM, SamplingParams
# vLLM auto-detects AWQ models
llm = LLM(
model="TheBloke/Llama-2-7B-AWQ",
quantization="awq",
dtype="half"
)
sampling = SamplingParams(temperature=0.7, max_tokens=200)
outputs = llm.generate(["Explain AI"], sampling)Performance benchmarks
Memory reduction
| Model | FP16 | AWQ 4-bit | Reduction |
|---|---|---|---|
| Mistral 7B | 14 GB | 5.5 GB | 2.5x |
| Llama 2-13B | 26 GB | 10 GB | 2.6x |
| Llama 2-70B | 140 GB | 35 GB | 4x |
Inference speed (RTX 4090)
| Model | Prefill (tok/s) | Decode (tok/s) | Memory |
|---|---|---|---|
| Mistral 7B GEMM | 3,897 | 114 | 5.55 GB |
| TinyLlama 1B GEMV | 5,179 | 431 | 2.10 GB |
| Llama 2-13B GEMM | 2,279 | 74 | 10.28 GB |
Accuracy (perplexity)
| Model | FP16 | AWQ 4-bit | Degradation |
|---|---|---|---|
| Llama 3 8B | 8.20 | 8.48 | +3.4% |
| Mistral 7B | 5.25 | 5.42 | +3.2% |
| Qwen2 72B | 4.85 | 4.95 | +2.1% |
Custom calibration data
python
# Use custom dataset for domain-specific models
model.quantize(
tokenizer,
quant_config=quant_config,
calib_data="wikitext", # Or custom list of strings
max_calib_samples=256, # More samples = better accuracy
max_calib_seq_len=512 # Sequence length
)
# Or provide your own samples
calib_samples = [
"Your domain-specific text here...",
"More examples from your use case...",
]
model.quantize(tokenizer, quant_config=quant_config, calib_data=calib_samples)Multi-GPU deployment
python
model = AutoAWQForCausalLM.from_quantized(
"TheBloke/Llama-2-70B-AWQ",
device_map="auto", # Auto-split across GPUs
max_memory={0: "40GB", 1: "40GB"}
)Supported models
35+ architectures including:
- Llama family: Llama 2/3, Code Llama, Mistral, Mixtral
- Qwen: Qwen, Qwen2, Qwen2.5-VL
- Others: Falcon, MPT, Phi, Yi, DeepSeek, Gemma
- Multimodal: LLaVA, LLaVA-Next, Qwen2-VL
Common issues
CUDA OOM during quantization:
python
# Reduce batch size
model.quantize(tokenizer, quant_config=quant_config, max_calib_samples=64)Slow inference:
python
# Enable fused layers
model = AutoAWQForCausalLM.from_quantized(model_name, fuse_layers=True)AMD GPU support:
python
# Use ExLlama backend
config = AwqConfig(bits=4, version="exllama")Deprecation notice
AutoAWQ is officially deprecated. For new projects, consider:
- vLLM llm-compressor: https://github.com/vllm-project/llm-compressor
- MLX-LM: For Mac devices with Apple Silicon
Existing quantized models remain usable.
References
- Paper: AWQ: Activation-aware Weight Quantization (arXiv:2306.00978) - MLSys 2024 Best Paper
- GitHub: https://github.com/casper-hansen/AutoAWQ
- MIT Han Lab: https://github.com/mit-han-lab/llm-awq
- Models: https://huggingface.co/models?library=awq
