mavis-ai/Multilingual-e5-large-Q8

This repository contains an 8-bit quantized MLX-compatible distribution of intfloat/multilingual-e5-large, prepared for use with R.E.V.I.S. as its local semantic embedding model.

The model is intended for local text embedding, semantic recall, RAG retrieval, and multilingual semantic search workflows.

Important Notice

This repository is hosted primarily as a dedicated download source for the R.E.V.I.S. application ecosystem. You are free to download and use this model package for your own local embedding or MLX workflows, subject to the MIT License and the attribution notices included in this repository.

This package is not a new embedding model and has not been fine-tuned. It is a quantized redistribution of intfloat/multilingual-e5-large.

For the original model card, training details, intended usage, and evaluation information, refer to the official upstream model:

• Original model: https://huggingface.co/intfloat/multilingual-e5-large
• Base architecture: XLM-RoBERTa large
• Embedding size: 1024

Quantization

This package stores selected 2D weight tensors using a R.E.V.I.S. Q8 format:

• Quantization type: symmetric per-row int8
• Scale format: per-row scale tensor
• Expected dequantization: weight = qweight.astype(float16) * scale[:, None].astype(float16)

Typical tensor layout:

encoder.layer.0.attention.self.query.weight.qweight
encoder.layer.0.attention.self.query.weight.scale

Non-quantized tensors, such as LayerNorm parameters, bias tensors, and other small metadata tensors, are preserved in their original floating-point representation.

This format is optimized for smaller download and storage size. In the current R.E.V.I.S. runtime, q8 tensors may be dequantized to floating point at load time for compatibility with the existing embedding forward path.

Optimized for R.E.V.I.S. (Local Cognitive OS)

We host this model package to serve as the local semantic embedding engine for R.E.V.I.S.

R.E.V.I.S. is a 100% local Cognitive OS for Multi-Agentic AI. It transforms your Mac devices into a distributed Agentic Swarm via zero-config Wi-Fi clustering, allowing you to run heavy AI workloads—like recursive web research, dynamic RAG generation, and multi-step logic—without killing single-machine performance.

If you are interested in pushing the absolute limits of local AI and open-weight models, check out our project.

• Official Website: https://mavis-ai.co.jp/revis/
• Watch the 13-min Raw Demo (Multi-node Dynamic RAG): https://x.gd/LxaBF
• Follow our updates on X: https://x.com/mavis_ai_jp

Usage Notes

For retrieval-style tasks, E5 models typically use different text prefixes for queries and passages. R.E.V.I.S. applies its own canonical query and passage formatting internally.

If you use this package outside R.E.V.I.S., refer to the upstream E5 instructions for recommended prompt prefixes and pooling behavior.

Files

Recommended repository files:

README.md
LICENSE
NOTICE
weights.00.safetensors
config.json
tokenizer.json
tokenizer_config.json
special_tokens_map.json
quantization.json

License

This repository redistributes a quantized package derived from intfloat/multilingual-e5-large, which is released under the MIT License.

The upstream copyright notice and MIT License text are preserved in LICENSE.

Additional attribution and redistribution notes are included in NOTICE.

Attribution

Original model:

intfloat/multilingual-e5-large
https://huggingface.co/intfloat/multilingual-e5-large

Original authors / associated paper:

Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei
Multilingual E5 Text Embeddings: A Technical Report

R.E.V.I.S. Q8 package:

Prepared and redistributed by MAVIS / R.E.V.I.S.
Quantization: symmetric per-row int8 Q8 package for local MLX embedding runtime

Modification Notice

Compared with the upstream intfloat/multilingual-e5-large release, this repository applies the following packaging modification:

Selected 2D weight tensors were quantized to symmetric per-row int8 q8 representation.

No fine-tuning, additional training, or architecture-level modification has been applied.