Instructions to use facebook/esm2_t6_8M_UR50D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/esm2_t6_8M_UR50D with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="facebook/esm2_t6_8M_UR50D")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D") model = AutoModelForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D") - Inference
- Notebooks
- Google Colab
- Kaggle
TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecture
#18 opened about 22 hours ago
by
vigneshwar234
Request: DOI
#17 opened 5 months ago
by
cla1r3
Request: DOI
#16 opened 9 months ago
by
SathiyajithX
Request: DOI
#15 opened over 1 year ago
by
Atharvab7
Request: DOI
#13 opened over 1 year ago
by
JK-slone
Attention matrix
1
#12 opened almost 3 years ago
by
stolosa
Lower precision
#11 opened almost 3 years ago
by
pipparichter
PEFT LoRA and QLoRA
#10 opened almost 3 years ago
by
AmelieSchreiber
accessing to embedding layer and generate embeddings step by step
#9 opened about 3 years ago
by
francescopatane
Understanding vocabulary size
#8 opened about 3 years ago
by
dannyLCG
how visualize attention matrix
2
#7 opened about 3 years ago
by
francescopatane
TorchScript export failed. Maybe related to sequence length cache.
#5 opened over 3 years ago
by
chenchaozhao
inferring device map for model
#4 opened over 3 years ago
by
mahdi-b
passing parameters to the underlying model's forward
4
#3 opened over 3 years ago
by
mahdi-b