nomic embed text v2 moe GGUF EU AI Act Compliance Profile

Llama.cpp Quantizations of nomic-embed-text-v2-moe: Multilingual Mixture of Experts Text Embeddings

Blog | Technical Report | AWS SageMaker | Atlas Embedding and Unstructured Data Analytics Platform

This model was presented in the paper Training Sparse Mixture Of Experts Text Embedding Models.

Using <a href="https://github.com/ggml-org/llama.cpp">llama.cpp</a> commit <a href="https://github.com/ggml-org/llama.cpp/tree/e3a9421b78da5c810d812e6348a405f5acc39f34">e3a9421b7</a> for quantization.

Original model: nomic-embed-text-v2-moe

Usage

This model can be used with the llama.cpp server and other software that supports llama.cpp embedding models.

Embedding text with nomic-embed-text requires task instruction prefixes at the beginning of each string.

For example, the code below shows how to use the search_query prefix to embed user questions, e.g. in a RAG application.

Start a llama.cpp server:

And run this code:

import requests

def dot(va, vb):
    return sum(a * b for a, b in zip(va, vb))

def embed(texts):
    resp = requests.post('http://localhost:8080/v1/embeddings', json={'input': texts}).json()
    return [d['embedding'] for d in resp['data']]

docs = ['嵌入很酷', '骆驼很酷']  # 'embeddings are cool', 'llamas are cool'
docs_embed = embed(['search_document: ' + d for d in docs])

query = '跟我讲讲嵌入'  # 'tell me about embeddings'
query_embed = embed(['search_query: ' + query])[0]
print(f'query: {query!r}')
for d, e in zip(docs, docs_embed):
    print(f'simi

Your obligations as a nomic embed text v2 moe GGUF deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Languages

Base Model