Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.
npxskills add zechenzhangagi/ai-research-skills--skill hqq-quantizationLoading…
Fast, calibration-free weight quantization supporting 8/4/3/2/1-bit precision with multiple optimized backends.
Use HQQ when:
Key advantages:
Use alternatives instead:
pip install hqq
# With specific backend
pip install hqq[torch] # PyTorch backend
pip install hqq[torchao] # TorchAO int4 backend
pip install hqq[bitblas] # BitBlas backend
pip install hqq[marlin] # Marlin backend
from hqq.core.quantize import BaseQuantizeConfig, HQQLinear
import torch.nn as nn
# Configure quantization
config = BaseQuantizeConfig(
nbits=4, # 4-bit quantization
group_size=64, # Group size for quantization
axis=1 # Quantize along output dimension
)
# Quantize a linear layer
linear = nn.Linear(4096, 4096)
hqq_linear = HQQLinear(linear, config)
# Use normally
output = hqq_linear(input_tensor)
from transformers import AutoModelForCausalLM, HqqConfig
# Configure HQQ
quantization_config = HqqConfig(
nbits=4,
group_size=64,
axis=1
)
# Load and quantize
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B",
quantization_config=quantization_config,
device_map="auto"
)
# Model is quantized and ready to use
HQQ uses BaseQuantizeConfig to define quantization parameters:
from hqq.core.quantize import BaseQuantizeConfig
# Standard 4-bit config
config_4bit = BaseQuantizeConfig(
nbits=4, # Bits per weight (1-8)
group_size=64, # Weights per quantization group
axis=1 # 0=input dim, 1=output dim
)
# Aggressive 2-bit config
config_2bit = BaseQuantizeConfig(
nbits=2,
group_size=16, # Smaller groups for low-bit
axis=1
)
# Mixed precision per layer type
layer_configs = {
"self_attn.q_proj": BaseQuantizeConfig(nbits=4, group_size=64),
"self_attn.k_proj": BaseQuantizeConfig(nbits=4, group_size=64),
"self_attn.v_proj": BaseQuantizeConfig(nbits=4, group_size=64),
"mlp.gate_proj": BaseQuantizeConfig(nbits=2, group_size=32),
"mlp.up_proj": BaseQuantizeConfig(nbits=2, group_size=32),
"mlp.down_proj": BaseQuantizeConfig(nbits=4, group_size=64),
}
The core quantized layer that replaces nn.Linear:
from hqq.core.quantize import HQQLinear
import torch
# Create quantized layer
linear = torch.nn.Linear(4096, 4096)
hqq_layer = HQQLinear(linear, config)
# Access quantized weights
W_q = hqq_layer.W_q # Quantized weights
scale = hqq_layer.scale # Scale factors
zero = hqq_layer.zero # Zero points
# Dequantize for inspection
W_dequant = hqq_layer.dequantize()
HQQ supports multiple inference backends for different hardware:
from hqq.core.quantize import HQQLinear
# Available backends
backends = [
"pytorch", # Pure PyTorch (default)
"pytorch_compile", # torch.compile optimized
"aten", # Custom CUDA kernels
"torchao_int4", # TorchAO int4 matmul
"gemlite", # GemLite CUDA kernels
"bitblas", # BitBlas optimized
"marlin", # Marlin 4-bit kernels
]
# Set backend globally
HQQLinear.set_backend("torchao_int4")
# Or per layer
hqq_layer.set_backend("marlin")
Backend selection guide:
| Backend | Best For | Requirements |
|---|---|---|
| pytorch | Compatibility | Any GPU |
| pytorch_compile | Moderate speedup | torch>=2.0 |
| aten | Good balance | CUDA GPU |
| torchao_int4 | 4-bit inference | torchao installed |
| marlin | Maximum 4-bit speed | Ampere+ GPU |
| bitblas | Flexible bit-widths | bitblas installed |
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load HQQ-quantized model from Hub
model = AutoModelForCausalLM.from_pretrained(
"mobiuslabsgmbh/Llama-3.1-8B-HQQ-4bit",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B")
# Use normally
inputs = tokenizer("Hello, world!", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=50)
from transformers import AutoModelForCausalLM, HqqConfig
# Quantize
config = HqqConfig(nbits=4, group_size=64)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B",
quantization_config=config,
device_map="auto"
)
# Save quantized model
model.save_pretrained("./llama-8b-hqq-4bit")
# Push to Hub
model.push_to_hub("my-org/Llama-3.1-8B-HQQ-4bit")
from transformers import AutoModelForCausalLM, HqqConfig
# Different precision per layer type
config = HqqConfig(
nbits=4,
group_size=64,
# Attention layers: higher precision
# MLP layers: lower precision for memory savings
dynamic_config={
"attn": {"nbits": 4, "group_size": 64},
"mlp": {"nbits": 2, "group_size": 32}
}
)
from vllm import LLM, SamplingParams
# Load HQQ-quantized model
llm = LLM(
model="mobiuslabsgmbh/Llama-3.1-8B-HQQ-4bit",
quantization="hqq",
dtype="float16"
)
# Generate
sampling_params = SamplingParams(temperature=0.7, max_tokens=100)
outputs = llm.generate(["What is machine learning?"], sampling_params)
from vllm import LLM
llm = LLM(
model="meta-llama/Llama-3.1-8B",
quantization="hqq",
quantization_config={
"nbits": 4,
"group_size": 64
}
)
from transformers import AutoModelForCausalLM, HqqConfig
from peft import LoraConfig, get_peft_model
# Load quantized model
quant_config = HqqConfig(nbits=4, group_size=64)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B",
quantization_config=quant_config,
device_map="auto"
)
# Apply LoRA
lora_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
model = get_peft_model(model, lora_config)
# Train normally with Trainer or custom loop
from transformers import TrainingArguments, Trainer
training_args = TrainingArguments(
output_dir="./hqq-lora-output",
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
learning_rate=2e-4,
num_train_epochs=3,
fp16=True,
logging_steps=10,
save_strategy="epoch"
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
data_collator=data_collator
)
trainer.train()
from transformers import AutoModelForCausalLM, AutoTokenizer, HqqConfig
# 1. Configure quantization
config = HqqConfig(nbits=4, group_size=64)
# 2. Load and quantize (no calibration needed!)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B",
quantization_config=config,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B")
# 3. Verify quality
prompt = "The capital of France is"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0]))
# 4. Save
model.save_pretrained("./llama-8b-hqq")
tokenizer.save_pretrained("./llama-8b-hqq")
from hqq.core.quantize import HQQLinear
from transformers import AutoModelForCausalLM, HqqConfig
# 1. Quantize with optimal backend
config = HqqConfig(nbits=4, group_size=64)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B",
quantization_config=config,
device_map="auto"
)
# 2. Set fast backend
HQQLinear.set_backend("marlin") # or "torchao_int4"
# 3. Compile for additional speedup
import torch
model = torch.compile(model)
# 4. Benchmark
import time
inputs = tokenizer("Hello", return_tensors="pt").to(model.device)
start = time.time()
for _ in range(10):
model.generate(**inputs, max_new_tokens=100)
print(f"Avg time: {(time.time() - start) / 10:.2f}s")
Out of memory during quantization:
# Quantize layer-by-layer
from hqq.models.hf.base import AutoHQQHFModel
model = AutoHQQHFModel.from_pretrained(
"meta-llama/Llama-3.1-8B",
quantization_config=config,
device_map="sequential" # Load layers sequentially
)
Slow inference:
# Switch to optimized backend
from hqq.core.quantize import HQQLinear
HQQLinear.set_backend("marlin") # Requires Ampere+ GPU
# Or compile
model = torch.compile(model, mode="reduce-overhead")
Poor quality at 2-bit:
# Use smaller group size
config = BaseQuantizeConfig(
nbits=2,
group_size=16, # Smaller groups help at low bits
axis=1
)
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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).