Instructions to use Synthyra/Profluent-E1-300M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/Profluent-E1-300M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Synthyra/Profluent-E1-300M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Synthyra/Profluent-E1-300M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Synthyra/Profluent-E1-300M: direct link, hf CLI and curl.
- Browser
- Download file 2 kB
-
https://huggingface.co/Synthyra/Profluent-E1-300M/resolve/main/config.json
- Command line
-
hf download hf://Synthyra/Profluent-E1-300M/config.json
-
curl -L -o config.json https://huggingface.co/Synthyra/Profluent-E1-300M/resolve/main/config.json
2 kB
| { | |
| "architectures": [ | |
| "E1ForMaskedLM" | |
| ], | |
| "attn_backend": "sdpa", | |
| "auto_map": { | |
| "AutoConfig": "modeling_fastplms.E1Config", | |
| "AutoModel": "modeling_fastplms.E1Model", | |
| "AutoModelForMaskedLM": "modeling_fastplms.E1ForMaskedLM", | |
| "AutoModelForSequenceClassification": "modeling_fastplms.E1ForSequenceClassification", | |
| "AutoModelForTokenClassification": "modeling_fastplms.E1ForTokenClassification" | |
| }, | |
| "bos_token_id": 1, | |
| "clip_qkv": 8, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 2, | |
| "fastplms_checkpoint_hash": "c29bdfd0241a0cd9685ed347bdd627018ecacff451baf0cd20674b64f44483f9", | |
| "fastplms_checkpoint_repo_id": "Synthyra/Profluent-E1-300M", | |
| "fastplms_checkpoint_revision": "5ef52c0ad2ae2578f40622696b763523810e8e26", | |
| "fastplms_model_id": "e1_300m", | |
| "fastplms_release_tool_revision": "e6dd397a9ad368c998d714f6bd64d40b533d1ed1", | |
| "fastplms_release_tool_sha256": "6d335c05aa49a232086a816deb25d248d1490529e5783acc3355b9bc6f03e0c2", | |
| "fastplms_runtime_bundle_sha256": "a8dda20d6a4a55e6808c7c2f2c9477c113edaa7d6ec6ef57f4a898cd47b73d48", | |
| "fastplms_runtime_revision": "e6dd397a9ad368c998d714f6bd64d40b533d1ed1", | |
| "fastplms_source_tree_sha256": "9e3cbab448ffde4fbbba79f17cf0321645584acd534c5f96c78b1017d9f3806b", | |
| "fastplms_weights_revision": "5ef52c0ad2ae2578f40622696b763523810e8e26", | |
| "gated_mlp": true, | |
| "global_attention_every_n_layers": 3, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "silu", | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "max_num_positions_global": 65536, | |
| "max_num_positions_within_seq": 8192, | |
| "max_num_sequences": 512, | |
| "model_type": "E1", | |
| "no_ffn_gradient_checkpointing": true, | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 20, | |
| "num_key_value_heads": 16, | |
| "pad_token_id": 0, | |
| "rms_norm_eps": 1e-05, | |
| "rope_theta_global": 500000.0, | |
| "rope_theta_within_seq": 10000.0, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "4.57.6", | |
| "use_cache": true, | |
| "vocab_size": 34 | |
| } | |