Instructions to use open-athena/marinfold-exp75 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use open-athena/marinfold-exp75 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("open-athena/marinfold-exp75", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: | |
| - biology | |
| - protein-language-model | |
| - marinfold | |
| # MarinFold exp75 checkpoints | |
| Final checkpoints from the MarinFold contacts-v1 1.5B tuning sweep tracked in | |
| [Open-Athena/MarinFold#75](https://github.com/Open-Athena/MarinFold/issues/75). | |
| Both checkpoint formats are retained: | |
| - `hf/` contains a `Qwen3ForCausalLM` Hugging Face safetensors export with the | |
| contacts-v1 tokenizer colocated with the weights. | |
| - `checkpoints/` contains the original Levanter OCDBT checkpoint for training | |
| restart or re-export. | |
| ## Checkpoint inventory | |
| | Run ID | Final step | Format | Repository path | GCS source | | |
| |---|---:|---|---|---| | |
| | `prot-exp75-cv1-1_5b-e8-lr1e-3-wd0p2-v1-bc3084` | 35679 | Hugging Face | [`hf/step-35679`](./prot-exp75-cv1-1_5b-e8-lr1e-3-wd0p2-v1-bc3084/hf/step-35679) | `gs://marin-us-east5/checkpoints/prot-exp75-cv1-1_5b-e8-lr1e-3-wd0p2-v1-bc3084/hf/step-35679/` | | |
| | `prot-exp75-cv1-1_5b-e8-lr1e-3-wd0p2-v1-bc3084` | 35679 | Levanter | [`checkpoints/step-35679`](./prot-exp75-cv1-1_5b-e8-lr1e-3-wd0p2-v1-bc3084/checkpoints/step-35679) | `gs://marin-us-east5/checkpoints/prot-exp75-cv1-1_5b-e8-lr1e-3-wd0p2-v1-bc3084/checkpoints/step-35679/` | | |
| W&B recorded this run as finished at step 35,679. The GCS run contains exactly | |
| one permanent Levanter checkpoint, also at step 35,679. | |