Instructions to use Synthyra/ESM3_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/ESM3_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Synthyra/ESM3_small", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Synthyra/ESM3_small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "FastESM3Model" | |
| ], | |
| "attn_backend": "sdpa", | |
| "auto_map": { | |
| "AutoConfig": "modeling_fastplms.FastESM3Config", | |
| "AutoModel": "modeling_fastplms.FastESM3Model" | |
| }, | |
| "fastplms_checkpoint_hash": "53c7d39ae7fa322e5cb8fd60e6a718f9f18da5de87689a9eee7c6b9a509d0cf2", | |
| "fastplms_checkpoint_repo_id": "Synthyra/ESM3_small", | |
| "fastplms_checkpoint_revision": "7ddb5a740f9e5f93933eb6410c0ee8684bc63ec1", | |
| "fastplms_model_id": "esm3_small", | |
| "fastplms_release_tool_revision": "e6dd397a9ad368c998d714f6bd64d40b533d1ed1", | |
| "fastplms_release_tool_sha256": "6d335c05aa49a232086a816deb25d248d1490529e5783acc3355b9bc6f03e0c2", | |
| "fastplms_runtime_bundle_sha256": "b02f11de9f863ef10745381a7dc9ba25a161eed3e41ab4d1712ce772d265ae1a", | |
| "fastplms_runtime_revision": "e6dd397a9ad368c998d714f6bd64d40b533d1ed1", | |
| "fastplms_source_tree_sha256": "5795b7580de3386dfc62c9caac57363beb4a8de9a6428fbbd6c62ac6c2e65f35", | |
| "fastplms_weights_revision": "7ddb5a740f9e5f93933eb6410c0ee8684bc63ec1", | |
| "hidden_size": 1536, | |
| "initializer_range": 0.02, | |
| "model_name": "esm3_sm_open_v1", | |
| "model_type": "fast_esm3", | |
| "num_attention_heads": 24, | |
| "num_hidden_layers": 48, | |
| "num_vector_heads": 256, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "4.57.6", | |
| "vocab_size": 64 | |
| } | |