hs-embed-1

hs-embed-1 is a multilingual text embedding model built for Hindsight.

Highlights

• Multilingual: English, Spanish, French, German, Portuguese, Japanese, Korean and Chinese (Simplified and Traditional).
• Variable dimensions: 384-dimensional vectors that can be truncated to 256, 128 or 64.
• Long inputs: up to 8,192 tokens.
• Small: 112M parameters. 98M of them are the token embedding table, so each forward pass runs a 13M-parameter transformer.
• No prompts: queries and documents are encoded the same way, with no prefix.
• CPU and GPU: ships an int8 ONNX export for CPU, and safetensors for GPU.
• Serves on TEI: CPU and GPU images.

Model overview

Evaluation

Serving on CPU

Usage

Sentence Transformers

pip install -U sentence-transformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("hindsight-ai/hs-embed-1")

queries = ["Where did Maya move last spring?"]
documents = [
    "Maya moved to Lisbon in April.",
    "Maya's brother lives in Porto.",
]

query_embeddings = model.encode(queries, normalize_embeddings=True)
document_embeddings = model.encode(documents, normalize_embeddings=True)

print(model.similarity(query_embeddings, document_embeddings))

For shorter vectors, load the model with truncate_dim:

model = SentenceTransformer("hindsight-ai/hs-embed-1", truncate_dim=256)

Text Embeddings Inference

CPU (uses onnx/model.onnx):

docker run -p 8080:80 \
  ghcr.io/huggingface/text-embeddings-inference:cpu-1.9.3 \
  --model-id hindsight-ai/hs-embed-1 --auto-truncate

GPU:

docker run --gpus all -p 8080:80 \
  ghcr.io/huggingface/text-embeddings-inference:89-1.9.3 \
  --model-id hindsight-ai/hs-embed-1 --auto-truncate

Use the GPU image tag for your GPU: 89-1.9.3 for L4 and other Ada GPUs, 1.9.3 for A100 and H100.

curl 127.0.0.1:8080/embed \
  -H 'Content-Type: application/json' \
  -d '{"inputs": ["Where did Maya move last spring?", "Maya moved to Lisbon in April."]}'

To use fewer dimensions, keep the first 256, 128 or 64 values of each vector and normalize again.

Hindsight

HINDSIGHT_API_EMBEDDINGS_PROVIDER=local
HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL=hindsight-ai/hs-embed-1

Or point Hindsight at a TEI server:

HINDSIGHT_API_EMBEDDINGS_PROVIDER=tei
HINDSIGHT_API_EMBEDDINGS_TEI_URL=http://localhost:8080

Hindsight Inference

hs-embed-1 is also served at https://inference.hindsight.vectorize.io. To use it from Hindsight:

HINDSIGHT_API_EMBEDDINGS_PROVIDER=hindsight
HINDSIGHT_API_EMBEDDINGS_HINDSIGHT_API_KEY=<your Hindsight Inference API key>

License

MIT

Citation

@misc{hindsightai2026hsembed1,
  title  = {hs-embed-1},
  author = {{Hindsight AI}},
  year   = {2026},
  url    = {https://huggingface.co/hindsight-ai/hs-embed-1}
}