Meta AI
Creator of the ESM protein language model family.
Protein embeddings2 models
Models on Rafflesia
ESM-2 8M
activeMIT
Create pooled or residue-level protein representations with the 6-layer, 8M-parameter ESM-2 checkpoint.
Protein embeddingsprotein_sequence → embedding_vector
$0.0001 / 1K residuesESM-2 35M
activeMIT
Create pooled or residue-level protein representations with the 12-layer, 35M-parameter ESM-2 checkpoint.
Protein embeddingsprotein_sequence → embedding_vector
$0.00025 / 1K residuesAbout
Meta AI's fundamental AI research group created the ESM family of protein language models, whose open weights became foundational infrastructure for protein machine learning.
On Rafflesia, the CPU-served ESM-2 embedding models are addressed explicitly at 8M and 35M parameters through the same provider/family/variant endpoint convention.
Use these models
Every endpoint speaks the same run lifecycle: POST the endpoint id and a typed input, get a run back with outputs, provenance, and measured billing.
run.sh
# one run lifecycle, every endpoint
curl https://api.rafflesia.ai/v1/runs \
-H "Authorization: Bearer $RAFFLESIA_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"endpoint": "meta-ai/esm2/8m/embed",
"input": { "sequence": "MKTAYIAK..." }
}'