Transformers
Safetensors
t5
text2text-generation
protein-language-model
fastplms
custom_code
text-generation-inference
Instructions to use Synthyra/ANKH_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/ANKH_base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Synthyra/ANKH_base", trust_remote_code=True) model = AutoModelForSeq2SeqLM.from_pretrained("Synthyra/ANKH_base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "ElnaggarLab/protx-base-1gspan-partreconstruction-20mlmp-encl48-decl24-ramd128-ranb64", | |
| "architectures": [ | |
| "T5ForConditionalGeneration" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "modeling_fastplms.FastAnkhConfig", | |
| "AutoModel": "modeling_fastplms.FastAnkhModel", | |
| "AutoModelForMaskedLM": "modeling_fastplms.FastAnkhForMaskedLMExtension", | |
| "AutoModelForSeq2SeqLM": "modeling_fastplms.FastAnkhForConditionalGeneration", | |
| "AutoModelForSequenceClassification": "modeling_fastplms.FastAnkhForSequenceClassification", | |
| "AutoModelForTokenClassification": "modeling_fastplms.FastAnkhForTokenClassification" | |
| }, | |
| "d_ff": 3072, | |
| "d_kv": 64, | |
| "d_model": 768, | |
| "decoder_start_token_id": 0, | |
| "dense_act_fn": "gelu_new", | |
| "dropout_rate": 0.0, | |
| "eos_token_id": 1, | |
| "fastplms_checkpoint_hash": "5b82812134aeb215463b6dcc5f0fb6659717dc8b94b73cad00a11c122a70a32b", | |
| "fastplms_checkpoint_repo_id": "ElnaggarLab/ankh-base", | |
| "fastplms_checkpoint_revision": "d99cb6b966530dfc2ae96bc69d9255c2a07308b0", | |
| "fastplms_model_id": "ankh_base", | |
| "fastplms_release_tool_revision": "e6dd397a9ad368c998d714f6bd64d40b533d1ed1", | |
| "fastplms_release_tool_sha256": "6d335c05aa49a232086a816deb25d248d1490529e5783acc3355b9bc6f03e0c2", | |
| "fastplms_runtime_bundle_sha256": "437c5f5dcc809678e99b8e36d962e78b7960170c81d0b6289d2baeef7920e40f", | |
| "fastplms_runtime_revision": "e6dd397a9ad368c998d714f6bd64d40b533d1ed1", | |
| "fastplms_source_tree_sha256": "44f108a32fffbe0e689434fbff109aea7d03a7085a550ed5771f2eac434f130d", | |
| "fastplms_weights_revision": "d99cb6b966530dfc2ae96bc69d9255c2a07308b0", | |
| "feed_forward_proj": "gated-gelu", | |
| "initializer_factor": 1.0, | |
| "is_encoder_decoder": true, | |
| "is_gated_act": true, | |
| "layer_norm_epsilon": 1e-06, | |
| "model_type": "t5", | |
| "num_decoder_layers": 24, | |
| "num_heads": 12, | |
| "num_layers": 48, | |
| "output_past": true, | |
| "pad_token_id": 0, | |
| "relative_attention_max_distance": 128, | |
| "relative_attention_num_buckets": 64, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.21.1", | |
| "use_cache": true, | |
| "vocab_size": 144 | |
| } | |