multilingual-e5-large-instruct is an embedding model for text embeddings from intfloat, published on Hugging Face as intfloat/multilingual-e5-large-instruct. The recommended 1.1 GB file needs at least 8 GB of memory (a file size estimate, not a tested run). Hugging Bay hosts 16 files for this model. License: MIT.
What we checked
File: check availability in Files.
Memory: Fit unknown. File size needed to check fit.
Last recorded run: none for this selected file.
Recorded checks do not guarantee safety or performance on your computer.
See file checks and sourcesQuick answer: compatibility and sources
Can I run multilingual-e5-large-instruct on my computer?
It depends on your memory, processor, file format and runtime. A download is not confirmed by the current file evidence; inspect the file record before trying it. A file-size fit is an estimate, not a successful run or speed promise. Read the license before use.
multilingual-e5-large-instruct is an embedding model for text embeddings. Model weights are learned parameters; a runtime loads them. A hosted file is an individual file Hugging Bay can serve. SHA-256 identifies exact file bytes. The availability date above applies to this file record, not the upstream metadata update date.
As of the recorded file availability check on .
Recorded SHA-256 for model.safetensors: dd6b6e4f52db0a7aff83a13d10e6c5342ef9f6ab799bad3221f4b35ef390fa85. Exact file evidence · Machine-readable source facts · Run evidence and any recorded receipts. A checksum fingerprints only the named file bytes; it does not prove safety, quality, a complete multi-file selection or a successful run.
Page text updated .
Model metadata: As of at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked .
Key facts
- Catalog record
- Summarizes upstream metadata; it is not itself a download or run receipt.
- File evidence
- Lists exact filenames, availability and any recorded SHA-256 for individual files.
- Run evidence
- Reports measured results only when a receipt exists for named file bytes and a setup; otherwise unmeasured.
- Checksum scope
- SHA-256 fingerprints one file’s bytes; it does not prove safety, quality or a multi-file bundle.
Evidence you can inspect (plain text)
- This page’s quick answer names the file record for hf-model-intfloat-multilingual-e5-large-instruct.
- Open /artifact/hf-model-intfloat-multilingual-e5-large-instruct/files for exact filenames, availability and any recorded SHA-256 for this model.
- Open /evidence/artifacts/hf-model-intfloat-multilingual-e5-large-instruct for run-readiness evidence tied to that record; absent receipts stay unmeasured.
- Open /api/artifacts/hf-model-intfloat-multilingual-e5-large-instruct/card.md for machine-readable source facts (not a runtime test).
- A matching checksum on this page fingerprints only the named bytes shown in file evidence.
Unmeasured states are stated explicitly; this page does not invent run scores or compatibility guarantees.
Which date applies to which fact
- File availability check:
- Committed page-copy sources:
- Upstream model metadata: Dates on artifact records when shown; not implied by page-copy revision
- Run receipts: Only on /evidence pages when recorded; otherwise unmeasured
Page-copy revision (Updated line above) identifies committed text sources, not upstream model edits or test outcomes.
Requirements and source record
intfloat/multilingual-e5-large-instructContent controls: not assessed.
The catalog lists this model for turning text into vectors. Catalog sources: license label, model card license, task label, model card task.
This model is a multilingual text embedding model with 24 layers and a 1024 dimensional embedding size. It was initialized from xlm roberta large, continually trained on multilingual datasets, and it is described with query and passage encoding examples for retrieval tasks.
Recorded model facts
- Repository
- intfloat/multilingual-e5-large-instruct
- Task
- feature-extraction
- License
- mit
- Framework
- sentence-transformers
19 files.
- Format
- safetensors · ONNX
Fit and run
Fit We can't estimate memory for this model yet.
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Where this number comes from
No eligible GGUF files are listed in this view. Check the file list for other formats.
Disk space on this computer is not measured.
File size multiplied by 1.3 provides an estimate of memory needed. Runtime, context length and other apps may need more. This is not a tested run.
Use another tool
Download a compatible GGUF file first. Tool and model compatibility vary.
- Ollama: write a Modelfile with FROM pointing to the GGUF, then run
ollama create my-model -f Modelfileandollama run my-model. - llama.cpp: install llama.cpp and load the GGUF with
llama-cli -mfollowed by its file path; use the model’s chat template if available. - LM Studio: import the GGUF from the downloaded file and load it in the app.
Dateien
All files and download checks →Where this information comes from
Enthaltene Dateien
Download availability is being checked.
Über dieses Modell
External Hugging Face catalog record for intfloat/multilingual-e5-large-instruct. Task: feature extraction. Library: sentence transformers. Languages: multilingual, af, am, ar, as, az, be, bg. Includes 12 model-card evaluation signals. Files we host have a published SHA-256.
Kurzinfo
- Hosted here
- Download availability is being checked.
- Full repo upstream
- unknown
- Recommended file
- 1.12 GB · model.safetensors
- Languages
- multilingual, af, am, ar, as, az, be, bg, bn, br, bs, ca
- Format
- safetensors · ONNX
- File hashes
- Hashes recorded. Date not recorded.
Lizenz und Quelle
License: mit
Check the license text and keep the required notices when you use or share the files.
Upstream source →- Task
- text embeddings
- Hosted here
- 16 files, 1.16 GB
- Full repo upstream
- unknown
- Recommended file
- 1.12 GB · model.safetensors
- Files
- 16 hosted of 19 listed
- Download
- Download here (16 hosted files)
- Added to Hugging Bay
- Updated at source
FAQ
What is this model?
intfloat/multilingual-e5-large-instruct is an embedding model for text embeddings published by intfloat, added to Hugging Bay Jun 29, 2026. Model from Hugging Face for feature extraction: intfloat/multilingual-e5-large-instruct · sentence transformers
Can I use it commercially?
It is released under the mit license. Commercial use is allowed. Permissive open license: commercial use, modification, and redistribution are permitted.
How big is it?
Hosted here: 16 files, 1.16 GB. The full repository size is not recorded. The recommended download, model.safetensors, is 1.12 GB.
What can I download here?
Hosted here: 16 files, 1.16 GB. Full repo upstream: unknown. The original release is at https://huggingface.co/intfloat/multilingual-e5-large-instruct.
File checks and computer fit
File pattern scans
Automated file-pattern scans flagged nothing; not a safety guarantee. 16 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-intfloat-multilingual-e5-large-instruct/safety on Hugging Bay.
Will intfloat/multilingual-e5-large-instruct run on my computer?
We can’t check the fit; we don’t know your machine. We don’t have your machine’s specs, so no check ran and no verdict is shown. Answer two quick questions and we’ll estimate, from the memory you tell us, whether it fits your machine. Or skip it; you can still download; fit isn’t checked.
Device fit from run reports
No device run reports recorded yet. Device fit has not been measured.
Run report details
Results describe the reported file and hardware, not a guarantee for your machine. Community reports have not been independently checked.
Run reports as JSON