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+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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+http://www.apache.org/licenses/LICENSE-2.0
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+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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+*This model was published in HF papers on 2024-08-23 and contributed to Hugging Face Transformers on 2024-08-27.*
+
+<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">
+<img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
+</div>
+
+# Granite
+
+[Granite](https://huggingface.co/papers/2408.13359) is a 3B parameter language model trained with the Power scheduler. Discovering a good learning rate for pretraining large language models is difficult because it depends on so many variables (batch size, number of training tokens, etc.) and it is expensive to perform a hyperparameter search. The Power scheduler is based on a power-law relationship between the variables and their transferability to larger models. Combining the Power scheduler with Maximum Update Parameterization (MUP) allows a model to be pretrained with one set of hyperparameters regardless of all the variables.
+
+You can find all the original Granite checkpoints under the [IBM-Granite](https://huggingface.co/ibm-granite) organization.
+
+> [!TIP]
+> Click on the Granite models in the right sidebar for more examples of how to apply Granite 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
+
+
+pipe = pipeline(
+    task="text-generation",
+    model="ibm-granite/granite-3.3-2b-base",
+    device=0
+)
+pipe("Explain quantum computing in simple terms ", max_new_tokens=50)
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-3.3-2b-base")
+model = AutoModelForCausalLM.from_pretrained(
+    "ibm-granite/granite-3.3-2b-base",
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+
+inputs = tokenizer("Explain quantum computing in simple terms", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_length=50, cache_implementation="static")
+print(tokenizer.decode(outputs[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 [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to int4.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+
+quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-3.3-8b-base")
+model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-3.3-8b-base", device_map="auto", attn_implementation="sdpa", quantization_config=quantization_config)
+
+inputs = tokenizer("Explain quantum computing in simple terms", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_length=50, cache_implementation="static")
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+
+tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-3.3-2b-base")
+model = AutoModelForCausalLM.from_pretrained(
+    "ibm-granite/granite-3.3-2b-base",
+    device_map="auto",
+    attn_implementation="sdpa",
+    quantization_config=quantization_config,
+)
+
+input_ids = tokenizer("Explain artificial intelligence to a 10 year old", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_length=50, cache_implementation="static")
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## GraniteConfig
+
+[[autodoc]] GraniteConfig
+
+## GraniteModel
+
+[[autodoc]] GraniteModel
+    - forward
+
+## GraniteForCausalLM
+
+[[autodoc]] GraniteForCausalLM
+    - forward