bitsandbytes

name	bitsandbytes
domain	ai-ml
description	BitsAndBytes for quantization of LLMs. Use for 8-bit and 4-bit model quantization, mixed INT8 quantization, NF4 quantization, and memory-efficient LLM loading. Best for running large models on limited GPU memory. For model loading use transformers; for efficient fine-tuning use peft; for distributed training use deepspeed.
license	MIT license
metadata	{"skill-author":"Eric Yiru"}

BitsAndBytes: Quantization for LLMs

Overview

BitsAndBytes provides quantization methods for efficient LLM loading and inference, including 8-bit (LLM.int8()) and 4-bit (NF4/FP4) quantization. Apply this skill for memory-efficient model loading, quantization-aware training, and running large models on limited GPU resources.

When to Use This Skill

This skill should be used when:

• Loading large language models in 8-bit or 4-bit
• Reducing GPU memory usage for inference
• Running 7B+ models on consumer GPUs
• Fine-tuning quantized models with PEFT
• Implementing mixed-precision quantization
• Optimizing inference latency
• Quantization-aware training
• Using NF4 format for better quality

Quick Start

Basic Import and Setup

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from bitsandbytes import BitsAndBytesConfig

8-bit Quantization

# Configure 8-bit quantization
quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0,  # Threshold for outlier detection
    llm_int8_has_fp16_weight=False,  # Keep fp16 for some weights
)

# Load model with 8-bit quantization
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    quantization_config=quantization_config,
    device_map="auto",
)

# Use model normally
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
inputs = tokenizer("Hello, my name is", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))

4-bit Quantization (NF4)

# Configure 4-bit NF4 quantization
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",  # NF4 format for better quality
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,  # Double quantization for more compression
)

# Load model with 4-bit quantization
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    quantization_config=quantization_config,
    device_map="auto",
)

# Generate
outputs = model.generate(**inputs)

Combined with PEFT

from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training

# Load quantized model
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    quantization_config=quantization_config,
    device_map="auto",
)

# Prepare for k-bit training
model = prepare_model_for_kbit_training(model)

# Apply LoRA
lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.CAUSAL_LM
)

model = get_peft_model(model, lora_config)

# Train normally
trainer = Trainer(model=model, ...)
trainer.train()

Quantization Configuration

8-bit Options

quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0,  # Outlier detection threshold
    llm_int8_skip_modules=None,  # Modules to skip quantization
    llm_int8_enable_fp32_cpu_offload=False,  # CPU offload for large models
)

4-bit Options

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",  # "nf4" or "fp4"
    bnb_4bit_compute_dtype=torch.float16,  # Compute dtype
    bnb_4bit_use_double_quant=True,  # Double quantization
    bnb_4bit_bits=4,  # Number of bits
)

Compute Dtypes

# Float16 (default)
bnb_4bit_compute_dtype=torch.float16

# BFloat16 (better numerical stability)
bnb_4bit_compute_dtype=torch.bfloat16

# Float32 (if needed)
bnb_4bit_compute_dtype=torch.float32

Advanced Features

CPU Offload

# For very large models that don't fit in GPU
quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_enable_fp32_cpu_offload=True,  # Offload to CPU
)

# This allows loading 30B+ models

Skip Specific Modules

# Don't quantize certain modules
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    llm_int8_skip_modules=["lm_head"],  # Keep output layer in fp16
)

Mixed Quantization

# Use different quantization for different parts
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    load_in_8bit=False,
    # Custom quantization for specific modules
)

Inference

Basic Inference

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from bitsandbytes import BitsAndBytesConfig

# Load quantized model
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    quantization_config=BitsAndBytesConfig(load_in_4bit=True),
    device_map="auto",
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")

# Generate
prompt = "Once upon a time"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=50,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Batch Inference

# Batch processing
prompts = [
    "Once upon a time",
    "In a galaxy far, far away",
    "It was the best of times",
]

inputs = tokenizer(prompts, return_tensors="pt", padding=True).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=50,
    do_sample=True,
)

results = [tokenizer.decode(o, skip_special_tokens=True) for o in outputs]

Training with Quantization

Prepare Model

from peft import prepare_model_for_kbit_training
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, Trainer

# Load quantized model
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    quantization_config=BitsAndBytesConfig(load_in_4bit=True),
    device_map="auto",
)

# Prepare for training
model = prepare_model_for_kbit_training(model)

# Apply LoRA
model = get_peft_model(model, lora_config)

# Train
trainer = Trainer(model=model, ...)
trainer.train()

Gradient Checkpointing

# Enable gradient checkpointing for memory efficiency
model.gradient_checkpointing_enable()

# May need to enable input require grads
model.enable_input_require_grads()

Memory Comparison

Method	7B Model	13B Model	70B Model
FP16	14GB	26GB	140GB
INT8	7GB	14GB	70GB
NF4	3.5GB	7GB	35GB
NF4 + QLoRA	~6GB VRAM	~12GB	~50GB

Common Pitfalls and Best Practices

1. Use NF4 over FP4: Better quality for LLMs
2. Enable double quantization: Minor quality loss, more memory savings
3. Match compute dtype: Use bfloat16 on modern GPUs
4. Device map: Use "auto" for automatic device placement
5. PEFT integration: Works seamlessly with QLoRA
6. Evaluate quality: Test quantized model against full precision
7. Don't quantize all models: Some models don't benefit
8. Inference speed: 4-bit may be slower due to dequantization
9. CPU offload: Use for very large models
10. Save properly: Use save_pretrained properly

Key Parameters

BitsAndBytesConfig

• load_in_8bit: Load model in 8-bit
• load_in_4bit: Load model in 4-bit
• bnb_4bit_quant_type: "nf4" or "fp4"
• bnb_4bit_compute_dtype: Computation dtype
• bnb_4bit_use_double_quant: Double quantization
• llm_int8_threshold: Outlier threshold for 8-bit
• llm_int8_skip_modules: Modules to skip

Additional Resources

• bitsandbytes GitHub: https://github.com/TimDettmers/bitsandbytes
• Quantization Paper: https://arxiv.org/abs/2208.07339
• NF4 Paper: https://arxiv.org/abs/2305.14314
• HuggingFace Blog: https://huggingface.co/blog/4bit-transformers

Tips for Effective Use

1. Start with 4-bit NF4: Best quality/compression ratio
2. Test inference: Verify output quality
3. Use with PEFT: QLoRA is very efficient
4. Monitor memory: Use nvidia-smi to check GPU usage
5. Batch size: Can increase with quantized models

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