Bharat-Embed 270M

Direct answer

Bharat-Embed 270M is a 270M text only EmbeddingGemma-2 fork for Hinglish and Hindi retrieval RAG. It loads without vision or audio, runs on CPU and ONNX INT8, truncates to 128d, and is Apache-2.0. Pair it with eulogik/flashrank-pro-base as reranker. Built by Eulogik.

Find with: embeddinggemma2 hindi hinglish indic rag on-device onnx gguf qdrant mrl apache 2.0.

Measured numbers

All numbers below were measured on a Mac mini M4, 2026-10-08/09. No estimates.

Check	Base text only	Bharat-Embed	Delta
Hindi IndicQA NDCG@10, 768d	0.7279	0.7324	+0.0045
Hindi cross lingual STS spearman	0.6430	0.6423	-0.0007 (tie)
Train loss start to end	0.586	0.236	learned
Torch vs ONNX INT8 mean drift	-	0.0066	under 1e-2 gate
Reload check	MISSING keys	clean, 168 adapters merged	fixed

Note: the mteb runs use the default mteb encoding path, same both sides. The +0.03 stretch gate was not met. This card reports the miss plainly. v1.1 retrains on diverse real Hindi.

Truncation cost on Hindi retrieval (measured NDCG@10)

Dims	Score	vs 768d
768	0.7324	-
512	0.7247	-0.008
256	0.7023	-0.030
128	0.6294	-0.103

Plain read: 512d is near lossless. 256d costs 0.03. 128d costs 0.10 on Hindi, steeper than the base card multilingual average. Pick 512 for quality, 256 for the cost balance. 128d only for tight budgets with eyes open.

How it was built

Fact	Value
Base	`google/embeddinggemma-2`, text only (`vision_config` None, `audio_config` None)
Method	LoRA r16 on attention plus MLP, merged to clean keys
Data	40k frozen triplets (15k Hinglish synth, 5k Banking77, 10k legal synth, 10k near-miss hard), SHA pinned
Train	3 epochs, batch 8, len 256, AdamW 2e-5, fp32, Mac mini M4
License note	FLORES eval only (CC-BY-SA). MS MARCO excluded (non-commercial terms). Train pack is Banking77 plus ours.

Quickstart

from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "eulogik/bharat-embed-270m-gemma2",
    config_kwargs={"vision_config": None, "audio_config": None},
    model_kwargs={"torch_dtype": "bfloat16"},
)
q = model.encode("GST refund kaise claim karein?", prompt_name="SearchQuery",
                 truncate_dim=256, normalize_embeddings=True)
d = model.encode("title: GST refund | text: apply through portal with invoice proof",
                 truncate_dim=256, normalize_embeddings=True)
print(model.similarity(q, d))

Rules: queries and docs share one dim. Always normalize after truncate. fp16 never (base warns NaN).

ONNX serve

pip install onnxruntime qdrant-client
python scripts/encode_onnx.py --model onnx/int8 --queries queries.txt --truncate 256

Limits

Text only. No image, video, or audio.
Hindi and Hinglish first. Tamil and Telugu unmeasured.
Trained at 256 tokens. Long doc behavior unmeasured.
Scores rank, they do not calibrate. Pair with a reranker for decisions.

Eulogik family

Reranker: eulogik/flashrank-pro-base (149M, Apache-2.0)
Code: github.com/eulogik/Bharat-Embed
Org: huggingface.co/eulogik

Keywords

embeddinggemma2 hindi embedding, hinglish embedding, indic rag embedding, on-device embedding, onnx embedding, gguf embedding ollama, qdrant hindi search, mrl matryoshka embedding, uae law embedding, difc rag