# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("SOTAagi2030/EarlyCheckpoint-v1")
model = AutoModel.from_pretrained("SOTAagi2030/EarlyCheckpoint-v1", device_map="auto")Quick Links
EarlyCheckpoint
1. Introduction
EarlyCheckpoint is the first saved checkpoint from our training run, captured at the very beginning of training. It serves as a baseline for comparing training progress.
This model represents the initial state of training and is useful for ablation studies and understanding training dynamics.
2. Model Information
| Property | Value |
|---|---|
| Architecture | BERT |
| Training Step | step_100 |
| License | Apache-2.0 |
3. How to Use
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("EarlyCheckpoint-v1")
tokenizer = AutoTokenizer.from_pretrained("EarlyCheckpoint-v1")
4. License
This model is licensed under the Apache-2.0 License.
5. Contact
Open an issue on our GitHub for questions.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SOTAagi2030/EarlyCheckpoint-v1")