Add StableLM (#28810)

* Add `StableLM`

* fix(model): re-create from `huggingface-cli add-new-model-like persimmon`

* fix: re-add changes to address comments

* fix(readme): add links to paper

* fix(tokenization_auto): remove `GPTNeoXTokenizerFastFast` ref

* fix(tests): re-add `@slow` decorator to integration tests

* fix(tests): import slow...

* fix(readme_hd): remove whitespace edit

* fix(tokenizer): auto tokenizer tuple

* skip doctests for `modeling_stablelm`

docs/source/en/model_doc/stablelm.md

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+
+# StableLM
+
+## Overview
+
+`StableLM 3B 4E1T` 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
+>>> device = "cuda" # the device to load the model onto
+
+>>> tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
+>>> model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t")
+>>> model.to(device)
+
+>>> 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 Santa Barbara and, for visitors hoping to make the move to our beautiful seaside city, this town offers plenty of great places to...']
+```
+
+## 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
+>>> import torch
+>>> from transformers import AutoModelForCausalLM, AutoTokenizer
+>>> device = "cuda" # the device to load the model onto
+
+>>> tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
+>>> model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2")
+>>> model.to(device)
+
+>>> 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 Santa Barbara and, for visitors hoping to make the move to our beautiful seaside city, this town offers plenty of great places to...']
+```
+
+
+## StableLmConfig
+
+[[autodoc]] StableLmConfig
+
+## StableLmModel
+
+[[autodoc]] StableLmModel
+    - forward
+
+## StableLmForCausalLM
+
+[[autodoc]] StableLmForCausalLM
+    - forward
+
+## StableLmForSequenceClassification
+
+[[autodoc]] StableLmForSequenceClassification
+    - forward