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+*This model was contributed to Hugging Face Transformers on 2024-02-14.*
+
+# StableLM
+
+<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>
+
+## Overview
+
+StableLM 3B 4E1T ([blog post](https://stability.ai/news/stable-lm-3b-sustainable-high-performance-language-models-smart-devices)) was proposed in [StableLM 3B 4E1T: Technical Report](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Stability AI and is the first model in a series of multi-epoch pre-trained language models.
+
+### Model Details
+
+StableLM 3B 4E1T is a decoder-only base language model pre-trained on 1 trillion tokens of diverse English and code datasets for four epochs.
+The model architecture is transformer-based with partial Rotary Position Embeddings, SwiGLU activation, LayerNorm, etc.
+
+We also provide StableLM Zephyr 3B, an instruction fine-tuned version of the model that can be used for chat-based applications.
+
+### Usage Tips
+
+- The architecture is similar to LLaMA but with RoPE applied to 25% of head embedding dimensions, LayerNorm instead of RMSNorm, and optional QKV bias terms.
+- `StableLM 3B 4E1T`-based models uses the same tokenizer as [`GPTNeoXTokenizerFast`].
+
+`StableLM 3B 4E1T` and `StableLM Zephyr 3B` can be found on the [Huggingface Hub](https://huggingface.co/stabilityai)
+
+The following code snippet demonstrates how to use `StableLM 3B 4E1T` for inference:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
+
+
+set_seed(0)
+
+tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
+model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", device_map="auto")
+
+model_inputs = tokenizer("The weather is always wonderful in", return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(**model_inputs, max_length=32, do_sample=True)
+responses = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+responses
+['The weather is always wonderful in Costa Rica, which makes it a prime destination for retirees. That’s where the Pensionado program comes in, offering']
+```
+
+## Combining StableLM and Flash Attention 2
+
+First, make sure to install the latest version of Flash Attention v2.
+
+```bash
+pip install -U flash-attn --no-build-isolation
+```
+
+Also make sure that your hardware is compatible with Flash-Attention 2. Read more about it in the official documentation of the [`flash-attn`](https://github.com/Dao-AILab/flash-attention) repository. Note: you must load your model in half-precision (e.g. `torch.bfloat16`).
+
+Now, to run the model with Flash Attention 2, refer to the snippet below:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
+
+
+set_seed(0)
+
+tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
+model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", attn_implementation="flash_attention_2", device_map="auto")  # doctest: +SKIP
+
+model_inputs = tokenizer("The weather is always wonderful in", return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(**model_inputs, max_length=32, do_sample=True)  # doctest: +SKIP
+responses = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)  # doctest: +SKIP
+responses  # doctest: +SKIP
+['The weather is always wonderful in Costa Rica, which makes it a prime destination for retirees. That’s where the Pensionado program comes in, offering']
+```
+
+## StableLmConfig
+
+[[autodoc]] StableLmConfig
+
+## StableLmModel
+
+[[autodoc]] StableLmModel
+    - forward
+
+## StableLmForCausalLM
+
+[[autodoc]] StableLmForCausalLM
+    - forward
+
+## StableLmForSequenceClassification
+
+[[autodoc]] StableLmForSequenceClassification
+    - forward
+
+## StableLmForTokenClassification
+
+[[autodoc]] StableLmForTokenClassification
+    - forward