Download README.md from awkeng/embeddinggemma-2-rad: direct link, hf CLI and curl.
- Browser
- Download file 2.39 kB
-
https://huggingface.co/awkeng/embeddinggemma-2-rad/resolve/main/README.md
- Command line
-
hf download hf://awkeng/embeddinggemma-2-rad/README.md
-
curl -L -o README.md https://huggingface.co/awkeng/embeddinggemma-2-rad/resolve/main/README.md
license: apache-2.0
base_model:
- google/embeddinggemma-2
base_model_relation: quantized
pipeline_tag: feature-extraction
tags:
- radiance
- embeddings
- multimodal
- sentence-similarity
- bf16
EmbeddingGemma 2 — radiance container (bf16)
google/embeddinggemma-2 packed, unchanged in
precision, into a single .rad container for the
radiance inference engine. Every weight stays bf16 as
the checkpoint ships it. This is an unofficial conversion; the model is Google DeepMind's, released
under Apache 2.0 (see LICENSE and the original model card).
| File | embeddinggemma2-bf16.rad — 1,502,429,184 bytes (1.39 GiB of weights, 1257 tensors) |
| SHA-256 | d46988383e41de05df1ff69f491b1a6351a2ab69d26771943ca150768115bdae |
| Source | google/embeddinggemma-2 at revision 914f7f89142e33e77833254d9c9b90c3cef7303b (model.safetensors SHA-256 197a32965d4b1105faf060417baa899e193fb73cd401f42ec9295234d5553d79) |
| Output | 768-dimensional pooled vectors (Matryoshka: dimensions may truncate) for text, images, video and audio |
| Engine | radiance 1.2.4 or later (embedding support); made and tested with 1.3.0 |
Serve
The model runs on the CPU (about 0.25–1.5 GiB of RAM) or on one GPU:
radiance --model embeddinggemma2-bf16.rad --host 0.0.0.0 --port 8000 --api-key "$KEY"
curl -s http://localhost:8000/v1/embeddings -H "Authorization: Bearer $KEY" \
-H 'Content-Type: application/json' \
-d '{"input":"free space path loss at 6 GHz","prompt_name":"query","dimensions":256}'
How this file was made
Converted with rad-convert from the official radiance image
docker.io/stilldeadcode/radiance:1.3.0@sha256:641f2d8dc30f26dcd42673916a480bb5d1a71b28de8cd5e7fb77b47f77251b8c,
with no quantisation recipe, so every weight keeps the checkpoint's encoding:
rad-convert ./embeddinggemma-2 --tokenizer ./embeddinggemma-2/tokenizer.json \
-o embeddinggemma2-bf16.rad
./embeddinggemma-2 is the source repository at the revision above. The conversion is
deterministic: running it again produces a byte-identical file with the SHA-256 above.
License
Apache License 2.0, as the original model. Copyright for the model belongs to Google DeepMind.