Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.
npxskills add zechenzhangagi/ai-research-skills--skill gptqLoading…
Post-training quantization method that compresses LLMs to 4-bit with minimal accuracy loss using group-wise quantization.
Use GPTQ when:
Use AWQ instead when:
Use bitsandbytes instead when:
# Install AutoGPTQ
pip install auto-gptq
# With Triton (Linux only, faster)
pip install auto-gptq[triton]
# With CUDA extensions (faster)
pip install auto-gptq --no-build-isolation
# Full installation
pip install auto-gptq transformers accelerate
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
# Load quantized model from HuggingFace
model_name = "TheBloke/Llama-2-7B-Chat-GPTQ"
model = AutoGPTQForCausalLM.from_quantized(
model_name,
device="cuda:0",
use_triton=False # Set True on Linux for speed
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Generate
prompt = "Explain quantum computing"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from datasets import load_dataset
# Load model
model_name = "meta-llama/Llama-2-7b-chat-hf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Quantization config
quantize_config = BaseQuantizeConfig(
bits=4, # 4-bit quantization
group_size=128, # Group size (recommended: 128)
desc_act=False, # Activation order (False for CUDA kernel)
damp_percent=0.01 # Dampening factor
)
# Load model for quantization
model = AutoGPTQForCausalLM.from_pretrained(
model_name,
quantize_config=quantize_config
)
# Prepare calibration data
dataset = load_dataset("c4", split="train", streaming=True)
calibration_data = [
tokenizer(example["text"])["input_ids"][:512]
for example in dataset.take(128)
]
# Quantize
model.quantize(calibration_data)
# Save quantized model
model.save_quantized("llama-2-7b-gptq")
tokenizer.save_pretrained("llama-2-7b-gptq")
# Push to HuggingFace
model.push_to_hub("username/llama-2-7b-gptq")
How GPTQ works:
Group size trade-off:
| Group Size | Model Size | Accuracy | Speed | Recommendation |
|---|---|---|---|---|
| -1 (per-column) | Smallest | Best | Slowest | Research only |
| 32 | Smaller | Better | Slower | High accuracy needed |
| 128 | Medium | Good | Fast | Recommended default |
| 256 | Larger | Lower | Faster | Speed critical |
| 1024 | Largest | Lowest | Fastest | Not recommended |
Example:
Weight matrix: [1024, 4096] = 4.2M elements
Group size = 128:
- Groups: 4.2M / 128 = 32,768 groups
- Each group: own 4-bit scale + zero-point
- Result: Better granularity → better accuracy
from auto_gptq import BaseQuantizeConfig
config = BaseQuantizeConfig(
bits=4, # 4-bit quantization
group_size=128, # Standard group size
desc_act=False, # Faster CUDA kernel
damp_percent=0.01 # Dampening factor
)
Performance:
config = BaseQuantizeConfig(
bits=3, # 3-bit (more compression)
group_size=128, # Keep standard group size
desc_act=True, # Better accuracy (slower)
damp_percent=0.01
)
Trade-off:
config = BaseQuantizeConfig(
bits=4,
group_size=32, # Smaller groups (better accuracy)
desc_act=True, # Activation reordering
damp_percent=0.005 # Lower dampening
)
Trade-off:
model = AutoGPTQForCausalLM.from_quantized(
model_name,
device="cuda:0",
use_exllama=True, # Use ExLlamaV2
exllama_config={"version": 2}
)
Performance: 1.5-2× faster than Triton
# Quantize with Marlin format
config = BaseQuantizeConfig(
bits=4,
group_size=128,
desc_act=False # Required for Marlin
)
model.quantize(calibration_data, use_marlin=True)
# Load with Marlin
model = AutoGPTQForCausalLM.from_quantized(
model_name,
device="cuda:0",
use_marlin=True # 2× faster on A100/H100
)
Requirements:
model = AutoGPTQForCausalLM.from_quantized(
model_name,
device="cuda:0",
use_triton=True # Linux only
)
Performance: 1.2-1.5× faster than CUDA backend
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load quantized model (transformers auto-detects GPTQ)
model = AutoModelForCausalLM.from_pretrained(
"TheBloke/Llama-2-13B-Chat-GPTQ",
device_map="auto",
trust_remote_code=False
)
tokenizer = AutoTokenizer.from_pretrained("TheBloke/Llama-2-13B-Chat-GPTQ")
# Use like any transformers model
inputs = tokenizer("Hello", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)
from transformers import AutoModelForCausalLM
from peft import prepare_model_for_kbit_training, LoraConfig, get_peft_model
# Load GPTQ model
model = AutoModelForCausalLM.from_pretrained(
"TheBloke/Llama-2-7B-GPTQ",
device_map="auto"
)
# Prepare for LoRA training
model = prepare_model_for_kbit_training(model)
# LoRA config
lora_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "v_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
# Add LoRA adapters
model = get_peft_model(model, lora_config)
# Fine-tune (memory efficient!)
# 70B model trainable on single A100 80GB
| Model | FP16 | GPTQ 4-bit | Reduction |
|---|---|---|---|
| Llama 2-7B | 14 GB | 3.5 GB | 4× |
| Llama 2-13B | 26 GB | 6.5 GB | 4× |
| Llama 2-70B | 140 GB | 35 GB | 4× |
| Llama 3-405B | 810 GB | 203 GB | 4× |
Enables:
| Precision | Tokens/sec | vs FP16 |
|---|---|---|
| FP16 | 25 tok/s | 1× |
| GPTQ 4-bit (CUDA) | 85 tok/s | 3.4× |
| GPTQ 4-bit (ExLlama) | 105 tok/s | 4.2× |
| GPTQ 4-bit (Marlin) | 120 tok/s | 4.8× |
| Model | FP16 | GPTQ 4-bit (g=128) | Degradation |
|---|---|---|---|
| Llama 2-7B | 5.47 | 5.55 | +1.5% |
| Llama 2-13B | 4.88 | 4.95 | +1.4% |
| Llama 2-70B | 3.32 | 3.38 | +1.8% |
Excellent quality preservation - less than 2% degradation!
# Automatic device mapping
model = AutoGPTQForCausalLM.from_quantized(
"TheBloke/Llama-2-70B-GPTQ",
device_map="auto", # Automatically split across GPUs
max_memory={0: "40GB", 1: "40GB"} # Limit per GPU
)
# Manual device mapping
device_map = {
"model.embed_tokens": 0,
"model.layers.0-39": 0, # First 40 layers on GPU 0
"model.layers.40-79": 1, # Last 40 layers on GPU 1
"model.norm": 1,
"lm_head": 1
}
model = AutoGPTQForCausalLM.from_quantized(
model_name,
device_map=device_map
)
# Offload some layers to CPU (for very large models)
model = AutoGPTQForCausalLM.from_quantized(
"TheBloke/Llama-2-405B-GPTQ",
device_map="auto",
max_memory={
0: "80GB", # GPU 0
1: "80GB", # GPU 1
2: "80GB", # GPU 2
"cpu": "200GB" # Offload overflow to CPU
}
)
# Process multiple prompts efficiently
prompts = [
"Explain AI",
"Explain ML",
"Explain DL"
]
inputs = tokenizer(prompts, return_tensors="pt", padding=True).to("cuda")
outputs = model.generate(
**inputs,
max_new_tokens=100,
pad_token_id=tokenizer.eos_token_id
)
for i, output in enumerate(outputs):
print(f"Prompt {i}: {tokenizer.decode(output)}")
TheBloke on HuggingFace:
Search:
# Find GPTQ models on HuggingFace
https://huggingface.co/models?library=gptq
Download:
from auto_gptq import AutoGPTQForCausalLM
# Automatically downloads from HuggingFace
model = AutoGPTQForCausalLM.from_quantized(
"TheBloke/Llama-2-70B-Chat-GPTQ",
device="cuda:0"
)
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).