transformers/docs/source/en/model_doc/granite.md

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# 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
import torch
from transformers import pipeline

pipe = pipeline(
    task="text-generation",
    model="ibm-granite/granite-3.3-2b-base",
    torch_dtype=torch.bfloat16,
    device=0
)
pipe("Explain quantum computing in simple terms ", max_new_tokens=50)
```

</hfoption>
<hfoption id="AutoModel">

```python
import torch
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",                                          
    torch_dtype=torch.bfloat16, 
    device_map="auto",
    attn_implementation="sdpa"
)

inputs = tokenizer("Explain quantum computing in simple terms", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=50, cache_implementation="static")
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</hfoption>
<hfoption id="transformers CLI">

```python
echo -e "Explain quantum computing simply." | transformers-cli run --task text-generation --model ibm-granite/granite-3.3-8b-instruct --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 [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to int4.

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, 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", torch_dtype=torch.bfloat16, device_map="auto", attn_implementation="sdpa", quantization_config=quantization_config)

inputs = tokenizer("Explain quantum computing in simple terms", return_tensors="pt").to("cuda")
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",
    torch_dtype=torch.bfloat16,
    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("cuda")
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