Add SAID-LAM model metadata #3836
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- SAID-LAM-v1: Linear Attention Memory with Crystalline Attention (SCA) - BETA - 32K context (licensed), 12K free tier - 384 dim embeddings - Perfect recall via deterministic attention - Organization: SAID Research
Samoed
requested changes
Jan 4, 2026
mteb/models/said_lam.py
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| from mteb import ModelMeta | ||
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| said_lam_v1 = ModelMeta( | ||
| name="SAID-LAM-v1", |
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You need to add code to run your model
Samoed
reviewed
Jan 4, 2026
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| from mteb import ModelMeta | ||
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| said_lam_v1 = ModelMeta( |
Member
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Please, try to load your model meta. Most of the fields are incorrect, or we don't have them
Contributor
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You can pre-generate the ModelMeta using:
from mteb.models import ModelMeta
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Qwen/Qwen3-Embedding-0.6B", device="cpu")
meta = ModelMeta.from_sentence_transformer_model(model)
print(meta.to_python())
If it is not sentence transfromers compatible do check out the docs.
5 tasks
- Added SAIDLAMEncoder class implementing MTEB's AbsEncoder protocol - loader=SAIDLAMEncoder (class reference, not function) - 384-dim embeddings with cosine similarity - Linear Attention Memory with SAID Crystalline Attention (SCA) - BETA - 32K context (licensed tier), 12K (free tier) - Tested on STS12 (75.02%)
Samoed
reviewed
Jan 6, 2026
| break | ||
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| try: | ||
| from lam import LAM |
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Can you update pyproject to include this package? I can't find it on PYPI
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