eeANE 1.2.0

eeANE 1.2.0 is a verification release: 35 more models were run end to end and are now documented, bringing the verified total to 46. There are no engine changes — if you are already running eeANE, nothing about your setup changes. What is new is that many more models are now known to work, and there is a table telling you which.

35 newly verified models

Each of these compiles with a single eeane compile <HF ID> and serves through the existing multi-model server. Every one passed its accuracy self-check against the FP32 original with unchanged thresholds, and — new in this release — had its output compared against the reference sentence-transformers / CrossEncoder implementation on an M2 Mac: embedding cosine ≥ 0.9999, reranker scores within 0.0015 with matching top-1 and rank order. Neural Engine placement ranged from 91.2% to 98.9%.

Embedding models

• IBM granite — granite-embedding-107m-multilingual, granite-embedding-278m-multilingual
• Snowflake Arctic Embed — xs, s, m, m-v1.5, l, and l-v2.0 (the last one an 8192-token model that keeps the full 128/512/1024 bucket set)
• thenlper GTE — gte-base, gte-large
• mixedbread-ai/mxbai-embed-large-v1
• sentence-transformers — all-MiniLM-L6-v2, all-MiniLM-L12-v2, multi-qa-MiniLM-L6-cos-v1, paraphrase-MiniLM-L6-v2, paraphrase-multilingual-MiniLM-L12-v2, paraphrase-multilingual-mpnet-base-v2
• intfloat e5 — e5-small-v2, e5-base-v2, e5-large-v2, multilingual-e5-small, multilingual-e5-large-instruct
• cl-nagoya ruri-v3 — the small variants 30m, 70m, 130m (Japanese; a 128-token request takes about 1 ms warm on the 30m)
• hotchpotch/bekko-embedding-v1-a25m
• BAAI bge v1.5 Chinese — bge-small-zh-v1.5, bge-base-zh-v1.5, bge-large-zh-v1.5, plus the original bge-small-en

Rerankers

• ibm-granite/granite-embedding-reranker-english-r2
• hotchpotch japanese-reranker-v2 — tiny, xsmall, small, base (the tiny one scores a 512-token pair in about 2.3 ms warm)

A Verified models table

The README now has a Verified models section: one table per architecture family, giving each model's type and the buckets eeane compile produces for it. Worth knowing when you read it — a model's name does not always tell you which backend it uses. paraphrase-multilingual-mpnet-base-v2 is an XLM-RoBERTa model despite the name, and multilingual-e5-small is a BERT one while its larger siblings are not. The table groups by what config.json actually declares.

Models built on these three architectures that are not listed are likely to work as well; the table records what has been run end to end, not a limit.

Two limitations now documented

• CLS-pooling ModernBERT embedding models are not supported yet. The ModernBERT backend compiles mean pooling only, so an embedding model of that family declaring CLS pooling would be compiled with the wrong pooling. The BERT and XLM-RoBERTa backends read the declared pooling and handle both, and ModernBERT rerankers are unaffected. Support is planned.
• Accuracy on out-of-vocabulary input. Compiled models run in fp16. For input a model's tokenizer cannot represent well — feeding English text to a Chinese-only model, say — the rounding difference against an fp32 reference grows noticeably. Within a model's intended languages the agreement stays tight.

The Chinese bge v1.5 models, previously listed as unverified, are now verified. Two of them ship pytorch_model.bin only, so they need --allow-pickle.

Requires Python 3.11 or 3.12, an Apple Silicon Mac on macOS 13+, and Xcode Command Line Tools for compiling models.