# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("SOTAagi2030/MidTraining-Checkpoint")
model = AutoModel.from_pretrained("SOTAagi2030/MidTraining-Checkpoint", device_map="auto")Quick Links
MidTrainingCheckpoint
1. Introduction
MidTrainingCheckpoint is a snapshot taken at the midpoint of our training run. It captures the model state at step 500, providing a useful reference for studying training dynamics.
This checkpoint is particularly useful for:
- Comparing with earlier and later checkpoints
- Understanding the training trajectory
- Performing intermediate model analysis
2. Model Details
| Property | Value |
|---|---|
| Architecture | BERT |
| Training Steps | 500 |
| Checkpoint Name | step_500 |
| Purpose | Mid-training reference |
3. Usage
from transformers import AutoModel
model = AutoModel.from_pretrained("MidTraining-Checkpoint")
4. License
5. Contact
Open an issue on GitHub.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SOTAagi2030/MidTraining-Checkpoint")