# Mamba
[Mamba](https://huggingface.co/papers/2312.00752) is a selective structured state space model (SSMs) designed to work around Transformers computational inefficiency when dealing with long sequences. It is a completely attention-free architecture, and comprised of a combination of H3 and gated MLP blocks (Mamba block). Mamba's "content-based reasoning" allows it to focus on specific parts of an input depending on the current token. Mamba also uses a new hardware-aware parallel algorithm to compensate for the lack of convolutional operations. As a result, Mamba has fast inference and can scale to very long sequences.
You can find all the original Mamba checkpoints under the [State Space Models](https://huggingface.co/state-spaces) organization.
> [!TIP]
> Click on the Mamba models in the right sidebar for more examples of how to apply Mamba to different language tasks.
The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and from the command line.
```py
import torch
from transformers import pipeline
pipeline = pipeline(
task="text-generation",
model="state-spaces/mamba-130m-hf",
torch_dtype=torch.float16,
device=0
)
pipeline("Plants create energy through a process known as")
```
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf")
model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-130m-hf", torch_dtype=torch.float16, device_map="auto",)
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to("cuda")
output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True)
```
```bash
echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model state-spaces/mamba-130m-hf --device 0
```
Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
The example below uses [torchao](../quantization/torchao) to only quantize the weights to 4-bit integers.
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
from torchao.quantization import Int4WeightOnlyConfig
quantization_config = Int4WeightOnlyConfig(group_size=128)
quantization_config = TorchAoConfig(quant_type=quant_config)
tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-2.8b-hf")
model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-2.8b-hf", torch_dtype=torch.bfloat16, quantization_config=quantization_config, device_map="auto",)
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to("cuda")
output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
## Notes
- The current implementation uses the original CUDA kernels. The FlashAttention equivalent implementation is hosted in the [mamba-ssm](https://github.com/state-spaces/mamba) and [causal_conv1d](https://github.com/Dao-AILab/causal-conv1d) repositories. Make sure to install them if your hardware supports it!
- Mamba stacks `mixer` layers which are equivalent to `Attention` layers. You can find the main logic of Mamba in the `MambaMixer` class.
- The example below demonstrates how to fine-tune Mamba with [PEFT](https://huggingface.co/docs/peft).
```py
from datasets import load_dataset
from trl import SFTConfig, SFTTrainer
from peft import LoraConfig
model_id = "state-spaces/mamba-130m-hf"
dataset = load_dataset("Abirate/english_quotes", split="train")
training_args = SFTConfig(dataset_text_field="quote")
lora_config = LoraConfig(target_modules=["x_proj", "embeddings", "in_proj", "out_proj"])
trainer = SFTTrainer(
model=model_id,
args=training_args,
train_dataset=dataset,
peft_config=lora_config,
)
trainer.train()
```
## MambaConfig
[[autodoc]] MambaConfig
## MambaModel
[[autodoc]] MambaModel
- forward
## MambaLMHeadModel
[[autodoc]] MambaForCausalLM
- forward
