orchestra-research/quantizing-models-bitsandbytes
Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.
npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill quantizing-models-bitsandbytesbitsandbytes reduces LLM memory by 50% (8-bit) or 75% (4-bit) with <1% accuracy loss.
Installation:
pip install bitsandbytes transformers accelerate
8-bit quantization (50% memory reduction):
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf",
quantization_config=config,
device_map="auto"
)
# Memory: 14GB → 7GB
4-bit quantization (75% memory reduction):
config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16
)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf",
quantization_config=config,
device_map="auto"
)
# Memory: 14GB → 3.5GB
Copy this checklist:
Quantization Loading:
- [ ] Step 1: Calculate memory requirements
- [ ] Step 2: Choose quantization level (4-bit or 8-bit)
- [ ] Step 3: Configure quantization
- [ ] Step 4: Load and verify model
Step 1: Calculate memory requirements
Estimate model memory:
FP16 memory (GB) = Parameters × 2 bytes / 1e9
INT8 memory (GB) = Parameters × 1 byte / 1e9
INT4 memory (GB) = Parameters × 0.5 bytes / 1e9
Example (Llama 2 7B):
FP16: 7B × 2 / 1e9 = 14 GB
INT8: 7B × 1 / 1e9 = 7 GB
INT4: 7B × 0.5 / 1e9 = 3.5 GB
Step 2: Choose quantization level
| GPU VRAM | Model Size | Recommended |
|----------|------------|-------------|
| 8 GB | 3B | 4-bit |
| 12 GB | 7B | 4-bit |
| 16 GB | 7B | 8-bit or 4-bit |
| 24 GB | 13B | 8-bit or 70B 4-bit |
| 40+ GB | 70B | 8-bit |
Step 3: Configure quantization
For 8-bit (better accuracy):
from transformers import BitsAndBytesConfig
import torch
config = BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_threshold=6.0, # Outlier threshold
llm_int8_has_fp16_weight=False
)
For 4-bit (maximum memory savings):
config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16, # Compute in FP16
bnb_4bit_quant_type="nf4", # NormalFloat4 (recommended)
bnb_4bit_use_double_quant=True # Nested quantization
)
Step 4: Load and verify model
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-13b-hf",
quantization_config=config,
device_map="auto", # Automatic device placement
torch_dtype=torch.float16
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-13b-hf")
# Test inference
inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))
# Check memory
import torch
print(f"Memory allocated: {torch.cuda.memory_allocated()/1e9:.2f}GB")
QLoRA enables fine-tuning large models on consumer GPUs.
Copy this checklist:
QLoRA Fine-tuning:
- [ ] Step 1: Install dependencies
- [ ] Step 2: Configure 4-bit base model
- [ ] Step 3: Add LoRA adapters
- [ ] Step 4: Train with standard Trainer
Step 1: Install dependencies
pip install bitsandbytes transformers peft accelerate datasets
Step 2: Configure 4-bit base model
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True
)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf",
quantization_config=bnb_config,
device_map="auto"
)
Step 3: Add LoRA adapters
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
# Prepare model for training
model = prepare_model_for_kbit_training(model)
# Configure LoRA
lora_config = LoraConfig(
r=16, # LoRA rank
lora_alpha=32, # LoRA alpha
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
# Add LoRA adapters
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# Output: trainable params: 4.2M || all params: 6.7B || trainable%: 0.06%
Step 4: Train with standard Trainer
from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(
output_dir="./qlora-output",
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
num_train_epochs=3,
learning_rate=2e-4,
fp16=True,
logging_steps=10,
save_strategy="epoch"
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
tokenizer=tokenizer
)
trainer.train()
# Save LoRA adapters (only ~20MB)
model.save_pretrained("./qlora-adapters")
Use 8-bit Adam/AdamW to reduce optimizer memory by 75%.
8-bit Optimizer Setup:
- [ ] Step 1: Replace standard optimizer
- [ ] Step 2: Configure training
- [ ] Step 3: Monitor memory savings
Step 1: Replace standard optimizer
import bitsandbytes as bnb
from transformers import Trainer, TrainingArguments
# Instead of torch.optim.AdamW
model = AutoModelForCausalLM.from_pretrained("model-name")
training_args = TrainingArguments(
output_dir="./output",
per_device_train_batch_size=8,
optim="paged_adamw_8bit", # 8-bit optimizer
learning_rate=5e-5
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset
)
trainer.train()
Manual optimizer usage:
import bitsandbytes as bnb
optimizer = bnb.optim.AdamW8bit(
model.parameters(),
lr=1e-4,
betas=(0.9, 0.999),
eps=1e-8
)
# Training loop
for batch in dataloader:
loss = model(**batch).loss
loss.backward()
optimizer.step()
optimizer.zero_grad()
Step 2: Configure training
Compare memory:
Standard AdamW optimizer memory = model_params × 8 bytes (states)
8-bit AdamW memory = model_params × 2 bytes
Savings = 75% optimizer memory
Example (Llama 2 7B):
Standard: 7B × 8 = 56 GB
8-bit: 7B × 2 = 14 GB
Savings: 42 GB
Step 3: Monitor memory savings
import torch
before = torch.cuda.memory_allocated()
# Training step
optimizer.step()
after = torch.cuda.memory_allocated()
print(f"Memory used: {(after-before)/1e9:.2f}GB")
Use bitsandbytes when:
Use alternatives instead:
Issue: CUDA error during loading
Install matching CUDA version:
# Check CUDA version
nvcc --version
# Install matching bitsandbytes
pip install bitsandbytes --no-cache-dir
Issue: Model loading slow
Use CPU offload for large models:
model = AutoModelForCausalLM.from_pretrained(
"model-name",
quantization_config=config,
device_map="auto",
max_memory={0: "20GB", "cpu": "30GB"} # Offload to CPU
)
Issue: Lower accuracy than expected
Try 8-bit instead of 4-bit:
config = BitsAndBytesConfig(load_in_8bit=True)
# 8-bit has <0.5% accuracy loss vs 1-2% for 4-bit
Or use NF4 with double quantization:
config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4", # Better than fp4
bnb_4bit_use_double_quant=True # Extra accuracy
)
Issue: OOM even with 4-bit
Enable CPU offload:
model = AutoModelForCausalLM.from_pretrained(
"model-name",
quantization_config=config,
device_map="auto",
offload_folder="offload", # Disk offload
offload_state_dict=True
)
QLoRA training guide: See references/qlora-training.md for complete fine-tuning workflows, hyperparameter tuning, and multi-GPU training.
Quantization formats: See references/quantization-formats.md for INT8, NF4, FP4 comparison, double quantization, and custom quantization configs.
Memory optimization: See references/memory-optimization.md for CPU offloading strategies, gradient checkpointing, and memory profiling.
Supported platforms: NVIDIA GPUs (primary), AMD ROCm, Intel GPUs (experimental)
Take orchestra-research/quantizing-models-bitsandbytes from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip.
Without those the skill loads but fails at the first command.