quantizing-models-bitsandbytes

bitsandbytes - LLM Quantization

Quick start

bitsandbytes 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

Common workflows

Workflow 1: Load large model in limited GPU memory

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")

Workflow 2: Fine-tune with QLoRA (4-bit training)

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")

Workflow 3: 8-bit optimizer for memory-efficient training

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")

When to use vs alternatives

Use bitsandbytes when:

• GPU memory limited (need to fit larger model)
• Training with QLoRA (fine-tune 70B on single GPU)
• Inference only (50-75% memory reduction)
• Using HuggingFace Transformers
• Acceptable 0-2% accuracy degradation

Use alternatives instead:

• GPTQ/AWQ: Production serving (faster inference than bitsandbytes)
• GGUF: CPU inference (llama.cpp)
• FP8: H100 GPUs (hardware FP8 faster)
• Full precision: Accuracy critical, memory not constrained

Common issues

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
)

Advanced topics

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.

Hardware requirements

• GPU: NVIDIA with compute capability 7.0+ (Turing, Ampere, Hopper)
• VRAM: Depends on model and quantization
  • 4-bit Llama 2 7B: 4GB
  • 4-bit Llama 2 13B: 8GB
  • 4-bit Llama 2 70B: 24GB
• CUDA: 11.1+ (12.0+ recommended)
• PyTorch: 2.0+

Supported platforms: NVIDIA GPUs (primary), AMD ROCm, Intel GPUs (experimental)

Resources

• GitHub: https://github.com/bitsandbytes-foundation/bitsandbytes
• HuggingFace docs: https://huggingface.co/docs/transformers/quantization/bitsandbytes
• QLoRA paper: "QLoRA: Efficient Finetuning of Quantized LLMs" (2023)
• LLM.int8() paper: "LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale" (2022)

Agent operating procedure

1. Check the environment. Check GPU type, memory and driver/CUDA versions (nvidia-smi), framework versions, and dataset location and size.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Do a smoke run: tiny model or subset, few steps, and confirm loss decreases and checkpoints save.
4. Execute the full task using the instructions and references above.
5. Validate the result. Track metrics on held-out data, compare against a baseline, and record seeds, configs and hardware.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
CUDA out-of-memory	Reduce batch size, enable gradient accumulation/checkpointing or mixed precision, or shard the model.
Loss is NaN or diverges	Lower the learning rate, check data for invalid values, and enable gradient clipping.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Never claim training results without logs; estimate compute cost before launching large jobs.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

• awq-quantization: Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss.
• gptq: Quantizes LLMs to 4-bit (also 3-bit) with GPTQ using group-wise quantization (group size 128 by default), via AutoGPTQ and transformers.
• hqq-quantization: Half-Quadratic Quantization for LLMs without calibration data.