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 hf_quant_config.json from ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental: direct link, hf CLI and curl.
- Browser
- Download file 248 Bytes
-
https://huggingface.co/ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental/resolve/main/hf_quant_config.json
- Command line
-
hf download hf://ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental/hf_quant_config.json
-
curl -L -o hf_quant_config.json https://huggingface.co/ibrahimkettaneh/Nemotron-3-Embed-8B-NVFP4-ModelOpt-PTQ-Experimental/resolve/main/hf_quant_config.json
248 Bytes
| { | |
| "producer": { | |
| "name": "modelopt", | |
| "version": "0.45.0" | |
| }, | |
| "quantization": { | |
| "quant_algo": "NVFP4", | |
| "kv_cache_quant_algo": null, | |
| "group_size": 16, | |
| "exclude_modules": [ | |
| "embed_tokens", | |
| "lm_head" | |
| ] | |
| } | |
| } | |