ChristianAzinn/mxbai-embed-large-v1-gguf

feature-extraction model by ChristianAzinn

Open original source ↗

From the publisher

Model card & documentation

Source preview

Read the publisher’s intended use, setup instructions, evaluations and limitations. The original model card is the source of truth.

Model creator: MixedBread AI Original model: mxbai-embed-large-v1 This is our base sentence embedding model. It was trained using AnglE loss on our high-quality large scale data. It achieves SOTA performance on BERT-large scale. Find out more in our blog post. This repo contains GGUF format files for the mxbai-embed-large-v1 embedding model. These files were converted and quantized with llama.cpp PR 5500, commit 34aa045de, on a consumer RTX 4090. This model supports up to 512 tokens of context. These files are compatible with llama.cpp as of commit 4524290e8, as well as LM Studio as of version 0.2.19. Click to see details The methods available are: Refer to the Provided Files table below to see what files use which methods, and how. To compute a single embedding, build llama.cpp and run: You can also submit a batch of texts to embed, as long as the total number of tokens does not exceed…

ChristianAzinn/mxbai-embed-large-v1-gguf on Hugging Face A short preview, not the full document.

Read the full model card ↗ · Preview checked 2026-10-30T20:33:04.608Z

Inside the original model card — Document outline
  1. mxbai-embed-large-v1-gguf
  2. Original Description
  3. Description
  4. Compatibility
  5. Meta-information
  6. Explanation of quantisation methods
  7. Provided Files
  8. Examples
  9. Example Usage with llama.cpp
  10. Example Usage with LM Studio
  11. Acknowledgements

Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.

Links from the model card

References supplied by the publisher, not independently verified endorsements. Check the destination before downloading files or entering credentials.

Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.

Model overview

ChristianAzinn/mxbai-embed-large-v1-gguf is a feature-extraction model repository published by ChristianAzinn on Hugging Face. The source reports the sentence-transformers library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task
feature-extraction
Library
sentence-transformers
Recent downloads (30 days)
209,050
Cumulative likes
8
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
Not reported
License
apache-2.0
Access
Not gated by Hugging Face
Created
2024-04-07T20:23:25.000Z
Last modified
2024-04-07T21:56:31.000Z

Compatibility and lineage

Reported languages: en

Reported base models: mixedbread-ai/mxbai-embed-large-v1

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

Use and evaluate

Check license terms and access requirements first. Review the model card’s documented loading instructions, evaluations and safety limitations. Benchmark scores are not imported by this directory, and popularity is not an accuracy ranking.

No model weights are downloaded or executed by AltAPIs. Never enable remote model code without reviewing it.

Model card and usage instructions

Model files and configuration

Community discussion

Source and freshness

Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:29.511Z. Daily imports are snapshots, not real-time monitoring.

Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.

Open original source

Related models

BAAI/bge-small-en-v1.5

BAAI/bge-large-en-v1.5

Qwen/Qwen3-Embedding-0.6B

BAAI/bge-base-en-v1.5

ibm-granite/granite-embedding-small-english-r2

intfloat/multilingual-e5-large