license: apache-2.0
language:
- en
- multilingual
base_model: zeroentropy/zembed-1-embedding
pipeline_tag: feature-extraction
library_name: sentence-transformers
tags:
- text-embeddings-inference
- infinity
model_max_length: 32768
zembed-1 for Infinity
Infinity configuration adaptation of ZeroEntropy's zembed-1-embedding, pinned to revision cf13c81f3274394053d166740294f7eea4586f7a. Model weights, tokenizer, pooling, and learned projection weights are unchanged. The original model card is retained in UPSTREAM_README.md, and the original license is retained in LICENSE.
modules.json replaces modeling_zembed.ZembedTransformer with the standard Sentence Transformers Transformer module so Infinity v3's LibTorch loader accepts the checkpoint. The original wrapper appends <|im_end|>\n to each input. Clients must still append this suffix; this copy does not make raw-text requests automatically equivalent to the original model.
Run
docker run --rm --gpus all -p 7997:80 -v infinity-hf-cache:/data \
ghcr.io/michaelfeil/infinity_v3:nvidia-3.0.0-alpha.3 \
--model-id michaelfeil/zembed-1-infinity \
--backend libtorch --torch-device cuda --device-id 0 \
--dtype bfloat16 --pooling last-token \
--default-prompt-name document --max-batch-tokens 32768
The saved query and document prompts provide the prefix. Supply the suffix exactly once, after the unformatted input text:
curl http://localhost:7997/embed \
-H 'Content-Type: application/json' \
-d '{"model":"michaelfeil/zembed-1-infinity","inputs":["What is backpropagation?<|im_end|>\n"],"prompt_name":"query","normalize":true,"truncate":false}'
Use prompt_name: "document" for documents. infinity_client.py adds the suffix automatically:
from infinity_client import embed
queries = embed(["What is backpropagation?"], kind="query")
documents = embed(["Backpropagation computes gradients."], kind="document")
This setup returns normalized, full-width 2560-dimensional embeddings with last-token pooling. The original learned dimensionality projections are preserved, but are not applied by this Infinity setup; simple truncation is not equivalent to those projections. Inputs above 32768 tokens should be chunked. Avoid truncating away the terminal suffix.
This repository is a configuration adaptation, not a new training run. Weight hashes are recorded in infinity-preparation.json. It has not yet undergone an end-to-end embedding parity run against the original Sentence Transformers model.