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# 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.

<hfoptions id="usage">
<hfoption id="Pipeline">

```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")
```

</hfoption>
<hfoption id="AutoModel">

```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)
```

</hfoption>
<hfoption id="transformers CLI">

```bash
echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model state-spaces/mamba-130m-hf --device 0
```

</hfoption>
</hfoptions>

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 SFTTrainer
   from peft import LoraConfig
   from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
   
   model_id = "state-spaces/mamba-130m-hf"
   tokenizer = AutoTokenizer.from_pretrained(model_id)
   model = AutoModelForCausalLM.from_pretrained(model_id)
   dataset = load_dataset("Abirate/english_quotes", split="train")
   training_args = TrainingArguments(
       output_dir="./results",
       num_train_epochs=3,
       per_device_train_batch_size=4,
       logging_dir='./logs',
       logging_steps=10,
       learning_rate=2e-3
   )
   lora_config =  LoraConfig(
           r=8,
           target_modules=["x_proj", "embeddings", "in_proj", "out_proj"],
           task_type="CAUSAL_LM",
           bias="none"
   )
   trainer = SFTTrainer(
       model=model,
       processing_class=tokenizer,
      args=training_args,
       peft_config=lora_config,
       train_dataset=dataset,
       dataset_text_field="quote",
   )
   trainer.train()
   ```

## MambaConfig

[[autodoc]] MambaConfig

## MambaModel

[[autodoc]] MambaModel
    - forward

## MambaLMHeadModel

[[autodoc]] MambaForCausalLM
    - forward
