Buckets:

hf-doc-build/doc-dev / trackio /pr_310 /en /transformers_integration.md
rtrm's picture
|
download
raw
1.43 kB
# Transformers Integration
Trackio integrates natively with Transformers so you can log metrics with minimal setup. Ensure you have the latest version of `transformers` installed (version 4.54.0 or higher).
```python
import numpy as np
from datasets import Dataset
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
# Create a fake dataset
data = np.random.randint(0, 1000, (8192, 64)).tolist()
dataset = Dataset.from_dict({"input_ids": data, "labels": data})
# Train a model using the Trainer API
trainer = Trainer(
model=AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B"),
args=TrainingArguments(run_name="Qwen3-0.6B-training", report_to="trackio"),
train_dataset=dataset,
)
trainer.train()
```
## Configuring Project and Space
You can specify your Trackio project name and space ID using environment variables:
```sh
export TRACKIO_PROJECT_NAME="my-project"
export TRACKIO_SPACE_ID="username/space_id"
```
Or set them directly in Python:
```python
import os
os.environ["TRACKIO_PROJECT_NAME"] = "my-project"
os.environ["TRACKIO_SPACE_ID"] = "username/space_id"
# rest of your code...
```
<iframe
src="https://trackio-documentation.hf.space/?project=transformers-integration&sidebar=hidden"
style="width: 100%; border:0;"
height="1530">
</iframe>
<EditOnGithub source="https://github.com/gradio-app/trackio/blob/main/docs/source/transformers_integration.md" />

Xet Storage Details

Size:
1.43 kB
·
Xet hash:
2e4102667c2ea40c39c3d14de7d5065575ce366fc15afbaf20fc6b783c0cc991

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.