Sentence Similarity
sentence-transformers
Safetensors
ministral3
text-embeddings
retrieval
rag
nvfp4
modelopt
vllm
8-bit precision
Instructions to use ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental/resolve/main/tokenizer.json
- Command line
-
hf download hf://ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- a01a432e74f09f5d253af5737a0c73dedca2eb567b9e9503e9eb5b1548d769d4
- Size of remote file:
- 17.1 MB
- SHA256:
- e03c58eceb178f1672537d5fc65e4322a12755bd6eeec1c17694aaa7528d7d27
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