slightly betterest model

README.md

@@ -1,14 +1,14 @@
 ---
 library_name: model2vec
 license: mit
-model_name: granite_125m_dim512_tl_distill-ckpt5
+model_name: granite_125m_dim512_tl_distill-ckpt6
 tags:
 - embeddings
 - static-embeddings
 - sentence-transformers
 ---
 
-# granite_125m_dim512_tl_distill-ckpt5 Model Card
+# granite_125m_dim512_tl_distill-ckpt6 Model Card
 
 This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of a Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.
 
@@ -31,7 +31,7 @@ Load this model using the `from_pretrained` method:
 from model2vec import StaticModel
 
 # Load a pretrained Model2Vec model
-model = StaticModel.from_pretrained("granite_125m_dim512_tl_distill-ckpt5")
+model = StaticModel.from_pretrained("granite_125m_dim512_tl_distill-ckpt6")
 
 # Compute text embeddings
 embeddings = model.encode(["Example sentence"])
@@ -45,7 +45,7 @@ You can also use the [Sentence Transformers library](https://github.com/UKPLab/s
 from sentence_transformers import SentenceTransformer
 
 # Load a pretrained Sentence Transformer model
-model = SentenceTransformer("granite_125m_dim512_tl_distill-ckpt5")
+model = SentenceTransformer("granite_125m_dim512_tl_distill-ckpt6")
 
 # Compute text embeddings
 embeddings = model.encode(["Example sentence"])

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 180093016
+oid sha256:de075e61ded0a0afb45042446b99bb0e6061c5b4795204487b4fa8e63f6843d7
+size 179963992