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 model.safetensors from ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental: direct link, hf CLI and curl.
- Browser
- Download file 5.25 GB
-
https://huggingface.co/ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental/resolve/main/model.safetensors
- Command line
-
hf download hf://ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental/resolve/main/model.safetensors
5.25 GB
- Xet hash:
- 0acc27b43134a01c77f2f10786bfd3f3950dcd8c1b4775ded8e5d8d70f89193d
- Size of remote file:
- 5.25 GB
- SHA256:
- c7e3090bffd0381550f95cd2ae60188fc150310b7379474008e943313c4c3f0d
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