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
File size: 914 Bytes
94a2ef2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"validation_type": "in-memory ModelOpt NVFP4 fake-quant graph",
"pairs": 24,
"lengths": {
"256": {
"mean_bf16_nvfp4_cosine": 0.9786386489868164,
"min_bf16_nvfp4_cosine": 0.9723175764083862,
"mean_score_abs_error": 0.007269359193742275,
"top1_agreement": 1.0,
"bf16_positive_top1": 1.0,
"nvfp4_positive_top1": 1.0
},
"1024": {
"mean_bf16_nvfp4_cosine": 0.9777471423149109,
"min_bf16_nvfp4_cosine": 0.9705635905265808,
"mean_score_abs_error": 0.008638310246169567,
"top1_agreement": 1.0,
"bf16_positive_top1": 1.0,
"nvfp4_positive_top1": 1.0
},
"4096": {
"mean_bf16_nvfp4_cosine": 0.9777042269706726,
"min_bf16_nvfp4_cosine": 0.971705436706543,
"mean_score_abs_error": 0.008743338286876678,
"top1_agreement": 1.0,
"bf16_positive_top1": 1.0,
"nvfp4_positive_top1": 1.0
}
}
}
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