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+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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+*This model was released on 2023-12-01 and added to Hugging Face Transformers on 2024-03-05.*
+
+<div style="float: right;">
+  <div class="flex flex-wrap space-x-1">
+    <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+  </div>
+</div>
+
+# 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]
+> This model was contributed by [Molbap](https://huggingface.co/Molbap) and [AntonV](https://huggingface.co/AntonV).
+> 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",
+    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", dtype=torch.float16, 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>
+<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", 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(model.device)
+
+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()
+   ```
+
+## MambaCache
+
+[[autodoc]] MambaCache
+    - update_conv_state
+    - update_ssm_state
+    - reset
+
+## MambaConfig
+
+[[autodoc]] MambaConfig
+
+## MambaModel
+
+[[autodoc]] MambaModel
+    - forward
+
+## MambaLMHeadModel
+
+[[autodoc]] MambaForCausalLM
+    - forward