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*This model was published in HF papers on 2024-05-31 and contributed to Hugging Face Transformers on 2024-08-06.*


# Mamba 2

[Mamba 2](https://huggingface.co/papers/2405.21060) is based on the state space duality (SSD) framework which connects structured state space models (SSMs) and attention variants. It uses a more efficient SSD algorithm that is 2-8x faster than Mamba and modifies the architecture to enable tensor parallelism and a grouped-value attention (GVA) head structure.

You can find all the original Mamba 2 checkpoints under the [State Space Models](https://huggingface.co/state-spaces) organization, but the examples shown below use [mistralai/Mamba-Codestral-7B-v0.1](https://huggingface.co/mistralai/Mamba-Codestral-7B-v0.1) because a Hugging Face implementation isn't supported yet for the original checkpoints.

Other Mamba 2-based architectures include [Bamba](./bamba), [FalconH1](./falcon_h1), and [Zamba2](./zamba2).

> [!TIP]
> This model was contributed by [ArthurZ](https://huggingface.co/ArthurZ).
> 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">

```python
from transformers import pipeline


pipeline = pipeline(
    task="text-generation",
    model="mistralai/Mamba-Codestral-7B-v0.1",
    device=0
)
pipeline("Plants create energy through a process known as")
```

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

```python
from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("mistralai/Mamba-Codestral-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mamba-Codestral-7B-v0.1", device_map="auto")
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)

output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</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.

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


quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mamba-Codestral-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mamba-Codestral-7B-v0.1", quantization_config=quantization_config, device_map="auto")
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)

output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Notes

- Codestral Mamba has `groups=8` which are similar to the number of kv heads in an attention-based model.
- Codestral Mamba has two different forward passes, `torch_forward` or `cuda_kernels_forward`, and their results are expected to be slightly different.
  - `torch_forward` without compilation is 3-4x faster than `cuda_kernels_forward`.
  - `cuda_kernels_forward` uses the original CUDA kernels if they're available in your environment. It is slower during prefill because it requires a "warmup run" due to the higher CPU overhead (see [these](https://github.com/state-spaces/mamba/issues/389#issuecomment-2171755306) [comments](https://github.com/state-spaces/mamba/issues/355#issuecomment-2147597457) for more details).

- There are no positional embeddings in this model, but there is an `attention_mask` and a specific logic to mask out hidden states in two places in the case of batched generation (see this [comment](https://github.com/state-spaces/mamba/issues/66#issuecomment-1863563829) for more details). This (and the addition of the reimplemented Mamba 2 kernels) results in a slight discrepancy between batched and cached generation.

- The SSM algorithm heavily relies on tensor contractions, which have matmul equivalents but the order of operations is slightly different. This makes the difference greater at smaller precisions.

- Hidden states that correspond to padding tokens is shutdown in 2 places and is mostly tested with left-padding. Right-padding propagates noise down the line and is not guaranteed to yield satisfactory results. `tokenizer.padding_side = "left"` ensures you are using the correct padding side.

- The example below demonstrates how to fine-tune Mamba 2 with [PEFT](https://huggingface.co/docs/peft).

```python
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTConfig, SFTTrainer


model_id = "mistralai/Mamba-Codestral-7B-v0.1"
dataset = load_dataset("Abirate/english_quotes", split="train")
training_args = SFTConfig(dataset_text_field="quote", gradient_checkpointing=True, per_device_train_batch_size=4)
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()
```

## Mamba2Config

[[autodoc]] Mamba2Config

## Mamba2Model

[[autodoc]] Mamba2Model
    - forward

## Mamba2LMHeadModel

[[autodoc]] Mamba2ForCausalLM
    - forward
