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# bitsandbytes

[bitsandbytes](https://github.com/TimDettmers/bitsandbytes) is the easiest option for quantizing a model to 8 and 4-bit. 8-bit quantization multiplies outliers in fp16 with non-outliers in int8, converts the non-outlier values back to fp16, and then adds them together to return the weights in fp16. This reduces the degradative effect outlier values have on a model's performance. 4-bit quantization compresses a model even further, and it is commonly used with [QLoRA](https://hf.co/papers/2305.14314) to finetune quantized LLMs.

To use bitsandbytes, make sure you have the following libraries installed:

<hfoptions id="bnb">
<hfoption id="8-bit">

```bash
pip install transformers accelerate bitsandbytes>0.37.0
```

</hfoption>
<hfoption id="4-bit">

```bash
pip install bitsandbytes>=0.39.0
pip install --upgrade accelerate transformers
```

</hfoption>
</hfoptions>

Now you can quantize a model by passing a `BitsAndBytesConfig` to [`~PreTrainedModel.from_pretrained`] method. This works for any model in any modality, as long as it supports loading with Accelerate and contains `torch.nn.Linear` layers.

<hfoptions id="bnb">
<hfoption id="8-bit">

Quantizing a model in 8-bit halves the memory-usage, and for large models, set `device_map="auto"` to efficiently use the GPUs available:

```py
from transformers import AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_8bit=True)

model_8bit = AutoModelForCausalLM.from_pretrained(
    "bigscience/bloom-1b7", 
    quantization_config=quantization_config
)
```

By default, all the other modules such as `torch.nn.LayerNorm` are converted to `torch.float16`. You can change the data type of these modules with the `torch_dtype` parameter if you want:

```py
import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_8bit=True)

model_8bit = AutoModelForCausalLM.from_pretrained(
    "facebook/opt-350m", 
    quantization_config=quantization_config, 
    torch_dtype=torch.float32
)
model_8bit.model.decoder.layers[-1].final_layer_norm.weight.dtype
```

Once a model is quantized to 8-bit, you can't push the quantized weights to the Hub unless you're using the latest version of Transformers and bitsandbytes. If you have the latest versions, then you can push the 8-bit model to the Hub with the [`~PreTrainedModel.push_to_hub`] method. The quantization config.json file is pushed first, followed by the quantized model weights.

```py
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_8bit=True)

model = AutoModelForCausalLM.from_pretrained(
    "bigscience/bloom-560m", 
    quantization_config=quantization_config
)
tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m")

model.push_to_hub("bloom-560m-8bit")
```

</hfoption>
<hfoption id="4-bit">

Quantizing a model in 4-bit reduces your memory-usage by 4x, and for large models, set `device_map="auto"` to efficiently use the GPUs available:

```py
from transformers import AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_4bit=True)

model_4bit = AutoModelForCausalLM.from_pretrained(
    "bigscience/bloom-1b7",
    quantization_config=quantization_config
)
```

By default, all the other modules such as `torch.nn.LayerNorm` are converted to `torch.float16`. You can change the data type of these modules with the `torch_dtype` parameter if you want:

```py
import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_4bit=True)

model_4bit = AutoModelForCausalLM.from_pretrained(
    "facebook/opt-350m",
    quantization_config=quantization_config, 
    torch_dtype=torch.float32
)
model_4bit.model.decoder.layers[-1].final_layer_norm.weight.dtype
```

If you have `bitsandbytes>=0.41.3`, you can serialize 4-bit models and push them on Hugging Face Hub. Simply call `model.push_to_hub()` after loading it in 4-bit precision. You can also save the serialized 4-bit models locally with `model.save_pretrained()` command.  

</hfoption>
</hfoptions>

<Tip warning={true}>

Training with 8-bit and 4-bit weights are only supported for training *extra* parameters.

</Tip>

You can check your memory footprint with the `get_memory_footprint` method:

```py
print(model.get_memory_footprint())
```

Quantized models can be loaded from the [`~PreTrainedModel.from_pretrained`] method without needing to specify the `load_in_8bit` or `load_in_4bit` parameters:

```py
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("{your_username}/bloom-560m-8bit", device_map="auto")
```

## 8-bit (LLM.int8() algorithm)

<Tip>

Learn more about the details of 8-bit quantization in this [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration)!

</Tip>

This section explores some of the specific features of 8-bit models, such as offloading, outlier thresholds, skipping module conversion, and finetuning.

### Offloading

8-bit models can offload weights between the CPU and GPU to support fitting very large models into memory. The weights dispatched to the CPU are actually stored in **float32**, and aren't converted to 8-bit. For example, to enable offloading for the [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) model, start by creating a [`BitsAndBytesConfig`]:

```py
from transformers import AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True)
```

Design a custom device map to fit everything on your GPU except for the `lm_head`, which you'll dispatch to the CPU:

```py
device_map = {
    "transformer.word_embeddings": 0,
    "transformer.word_embeddings_layernorm": 0,
    "lm_head": "cpu",
    "transformer.h": 0,
    "transformer.ln_f": 0,
}
```

Now load your model with the custom `device_map` and `quantization_config`:

```py
model_8bit = AutoModelForCausalLM.from_pretrained(
    "bigscience/bloom-1b7",
    device_map=device_map,
    quantization_config=quantization_config,
)
```

### Outlier threshold

An "outlier" is a hidden state value greater than a certain threshold, and these values are computed in fp16. While the values are usually normally distributed ([-3.5, 3.5]), this distribution can be very different for large models ([-60, 6] or [6, 60]). 8-bit quantization works well for values ~5, but beyond that, there is a significant performance penalty. A good default threshold value is 6, but a lower threshold may be needed for more unstable models (small models or finetuning).

To find the best threshold for your model, we recommend experimenting with the `llm_int8_threshold` parameter in [`BitsAndBytesConfig`]:

```py
from transformers import AutoModelForCausalLM, BitsAndBytesConfig

model_id = "bigscience/bloom-1b7"

quantization_config = BitsAndBytesConfig(
    llm_int8_threshold=10,
)

model_8bit = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map=device_map,
    quantization_config=quantization_config,
)
```

### Skip module conversion

For some models, like [Jukebox](model_doc/jukebox), you don't need to quantize every module to 8-bit which can actually cause instability. With Jukebox, there are several `lm_head` modules that should be skipped using the `llm_int8_skip_modules` parameter in [`BitsAndBytesConfig`]:

```py
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_id = "bigscience/bloom-1b7"

quantization_config = BitsAndBytesConfig(
    llm_int8_skip_modules=["lm_head"],
)

model_8bit = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    quantization_config=quantization_config,
)
```

### Finetuning

With the [PEFT](https://github.com/huggingface/peft) library, you can finetune large models like [flan-t5-large](https://huggingface.co/google/flan-t5-large) and [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) with 8-bit quantization. You don't need to pass the `device_map` parameter for training because it'll automatically load your model on a GPU. However, you can still customize the device map with the `device_map` parameter if you want to (`device_map="auto"` should only be used for inference).

## 4-bit (QLoRA algorithm)

<Tip>

Try 4-bit quantization in this [notebook](https://colab.research.google.com/drive/1ge2F1QSK8Q7h0hn3YKuBCOAS0bK8E0wf) and learn more about it's details in this [blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).

</Tip>

This section explores some of the specific features of 4-bit models, such as changing the compute data type, using the Normal Float 4 (NF4) data type, and using nested quantization.


### Compute data type

To speedup computation, you can change the data type from float32 (the default value) to bf16 using the `bnb_4bit_compute_dtype` parameter in [`BitsAndBytesConfig`]:

```py
import torch
from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)
```

### Normal Float 4 (NF4)

NF4 is a 4-bit data type from the [QLoRA](https://hf.co/papers/2305.14314) paper, adapted for weights initialized from a normal distribution. You should use NF4 for training 4-bit base models. This can be configured with the `bnb_4bit_quant_type` parameter in the [`BitsAndBytesConfig`]:

```py
from transformers import BitsAndBytesConfig

nf4_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
)

model_nf4 = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=nf4_config)
```

For inference, the `bnb_4bit_quant_type` does not have a huge impact on performance. However, to remain consistent with the model weights, you should use the `bnb_4bit_compute_dtype` and `torch_dtype` values.

### Nested quantization

Nested quantization is a technique that can save additional memory at no additional performance cost. This feature performs a second quantization of the already quantized weights to save an additional 0.4 bits/parameter. For example, with nested quantization, you can finetune a [Llama-13b](https://huggingface.co/meta-llama/Llama-2-13b) model on a 16GB NVIDIA T4 GPU with a sequence length of 1024, a batch size of 1, and enabling gradient accumulation with 4 steps.

```py
from transformers import BitsAndBytesConfig

double_quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
)

model_double_quant = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b", quantization_config=double_quant_config)
```

## Dequantizing `bitsandbytes` models

Once quantized, you can dequantize the model to the original precision but this might result in a small quality loss of the model. Make sure you have enough GPU RAM to fit the dequantized model. 

```python
from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer

model_id = "facebook/opt-125m"

model = AutoModelForCausalLM.from_pretrained(model_id, BitsAndBytesConfig(load_in_4bit=True))
tokenizer = AutoTokenizer.from_pretrained(model_id)

model.dequantize()

text = tokenizer("Hello my name is", return_tensors="pt").to(0)

out = model.generate(**text)
print(tokenizer.decode(out[0]))
```