Model reference · open weights
reve-positions is an open-weight embedding model from brain-bzh. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | brain-bzh |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 0M |
| Runs with | transformers |
| Released | 2025-10-31 |
| Popularity | 16k downloads / month |
| Licence | Unknown |
About
Wrapper to provide electrode positions to use the REVE EEG Foundation Model.
REVE (Representation for EEG with Versatile Embeddings) is a pretrained model explicitly designed to generalize across diverse EEG signals. REVE introduces a novel 4D positional encoding scheme that enables it to process signals of arbitrary length and electrode arrangement.
This position bank repository can be used to fetch electrode positions by name, in order to perform inference with the REVE modeL.
Example script to fetch electrode positions and extract embeddings with REVE.
from transformers import AutoModel
pos_bank = AutoModel.from_pretrained("brain-bzh/reve-positions", trust_remote_code=True)
eeg_data = ... # EEG data (batch_size, channels, time_points), must be sampled at 200 Hz
electrode_names = [...] # List of electrode names corresponding to the channels in eeg_data
positions = pos_bank(electrode_names) # Get positions (channels, 3)
model = AutoModel.from_pretrained("brain-bzh/reve-base", trust_remote_code=True)
## Expand the positions vector to match the batch size
positions = positions.expand(eeg_data.size(0), -1, -1) # (batch_size, channels, 3)
output = model(eeg_data, positions)
Available electrodes names can be printed using the method
pos_bank.get_all_positions(), and can be visualized here.
Most common electrode setups are available (10-20, 10-10, 10-05, EGI 256). For Biosemi-128, use the prefix biosemi128_ before the electrode names (e.g., biosemi128_C13).
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys reve-positions for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (reve-positions below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"reve-positions","input":"text to embed"}'
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