Supernova TeraLLM Embedding V2
Part of the Supernova TeraLLM Embedding family.
Architecture
Supernova V2
Second-generation lightweight Supernova embedding architecture for semantic retrieval.
Speciality
Stronger semantic retrieval.
Model size
Parameters: 6,978,688
This model is designed to be lightweight and suitable
for semantic retrieval experiments.
Supernova Embedding Family
| Model | Role |
|---|
| V1 | Balanced retrieval |
| V2 | Stronger semantic retrieval |
| V3 | Alternative architecture |
| V4 | Lightweight/high-speed retrieval |
Training
The models were trained as part of the Supernova
semantic retrieval research project.
The evaluation process includes:
- Recall@1
- Recall@3
- Recall@5
- MRR
- unseen-data evaluation
- speed testing
- comparison against Sentence Transformers
Intended use
This model is intended for:
- semantic search
- Nepali information retrieval
- retrieval-augmented generation
- document matching
- lightweight embedding experiments
Limitations
This is a research model.
Performance can vary depending on:
- dataset
- domain
- tokenizer coverage
- candidate distribution
- query style
The model should therefore be evaluated on
data appropriate to the intended application.
Supernova Project
Developed as part of the Supernova TeraLLM
embedding research project.
to use Supernova
import torch
from huggingface_hub import hf_hub_download
REPO_ID = "Supernova11c/Supernova-teraillm-Embedding-V2"
weights_path = hf_hub_download(
repo_id=REPO_ID,
filename="pytorch_model.bin"
)
state_dict = torch.load(
weights_path,
map_location="cpu"
)
model = SupernovaV2()
model.load_state_dict(
state_dict
)
model.eval()
