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+*This model was published in HF papers on 2024-11-22 and contributed to Hugging Face Transformers on 2025-01-27.*
+
+# Zamba2
+
+<div class="flex flex-wrap space-x-1">
+<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+[Zamba2](https://huggingface.co/papers/2411.15242) is a large language model (LLM) trained by Zyphra, and made available under an Apache 2.0 license. Please see the [Zyphra Hugging Face](https://huggingface.co/collections/zyphra/) repository for model weights.
+
+This model was contributed by [pglo](https://huggingface.co/pglo).
+
+## Model details
+
+[Zamba2-1.2B](https://www.zyphra.com/post/zamba2-mini), [Zamba2-2.7B](https://www.zyphra.com/post/zamba2-small) and [Zamba2-7B](https://www.zyphra.com/post/zamba2-7b) are hybrid models combining state-space models (Specifically [Mamba2](https://github.com/state-spaces/mamba)) and transformer, and were trained using next-token prediction. Zamba2 uses shared transformer layers after every 6 mamba blocks. It uses the [Mistral v0.1 tokenizer](https://huggingface.co/mistralai/Mistral-7B-v0.1). We came to this architecture after a series of ablations at small scales. Zamba2-1.2B, Zamba2-2.7B and Zamba2-7B were pre-trained on 2T and 3T tokens, respectively.
+
+<img src=https://github.com/user-attachments/assets/c2cff209-b901-483c-87aa-774b82a0769f width=30% height=40% />
+
+## Quick start
+
+### Presequities
+
+Zamba2 requires you use `transformers` version 4.48.0 or higher:
+
+```bash
+pip install transformers>=4.48.0
+```
+
+## Inference
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-7B")
+model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-7B", device_map="auto")
+
+input_text = "What factors contributed to the fall of the Roman Empire?"
+input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
+
+outputs = model.generate(**input_ids, max_new_tokens=100)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Model card
+
+The model cards can be found at:
+
+* [Zamba2-1.2B](https://huggingface.co/Zyphra/Zamba2-1.2B)
+* [Zamba2-2.7B](https://huggingface.co/Zyphra/Zamba2-2.7B)
+* [Zamba2-7B](https://huggingface.co/Zyphra/Zamba2-7B)
+
+## Issues
+
+For issues with model output, or community discussion, please use the Hugging Face community [forum](https://huggingface.co/Zyphra/Zamba2-7B/discussions)
+
+## License
+
+The model weights are open-sourced via an Apache 2.0 license.
+
+## Zamba2Config
+
+[[autodoc]] Zamba2Config
+
+## Zamba2Model
+
+[[autodoc]] Zamba2Model
+    - forward
+
+## Zamba2ForCausalLM
+
+[[autodoc]] Zamba2ForCausalLM
+    - forward
+
+## Zamba2ForSequenceClassification
+
+[[autodoc]] transformers.Zamba2ForSequenceClassification
+    - forward