*This model was released on {release_date} and added to Hugging Face Transformers on 2025-09-16.*
# OLMo3
Olmo3 is an improvement on [OLMo2](./olmo2). More details will be released on *soon*.
> [!TIP]
> Click on the OLMo3 models in the right sidebar for more examples of how to apply OLMo3 to different language tasks.
The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`] and from the command line.
```py
import torch
from transformers import pipeline
pipe = pipeline(
task="text-generation",
model="allenai/TBA",
dtype=torch.bfloat16,
device=0,
)
result = pipe("Plants create energy through a process known as")
print(result)
```
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"allenai/TBA"
)
model = AutoModelForCausalLM.from_pretrained(
"allenai/TBA",
dtype=torch.bfloat16,
device_map="auto",
attn_implementation="sdpa"
)
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)
output = model.generate(**input_ids, max_length=50, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
```bash
echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model allenai/TBA --device 0
```
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 [torchao](../quantization/torchao) to only quantize the weights to 4-bits.
```py
#pip install torchao
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
torchao_config = TorchAoConfig(
"int4_weight_only",
group_size=128
)
tokenizer = AutoTokenizer.from_pretrained(
"allenai/TBA"
)
model = AutoModelForCausalLM.from_pretrained(
"allenai/TBA",
quantization_config=torchao_config,
dtype=torch.bfloat16,
device_map="auto",
attn_implementation="sdpa"
)
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)
output = model.generate(**input_ids, max_length=50, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
## Notes
- Load specific intermediate checkpoints by adding the `revision` parameter to [`~PreTrainedModel.from_pretrained`].
```py
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("allenai/TBA", revision="stage1-step140000-tokens294B")
```
## Olmo3Config
[[autodoc]] Olmo3Config
## Olmo3ForCausalLM
[[autodoc]] Olmo3ForCausalLM
## Olmo3Model
[[autodoc]] Olmo3Model
- forward
## Olmo3PreTrainedModel
[[autodoc]] Olmo3PreTrainedModel
- forward
