granite model

config.yaml

@@ -53,3 +53,16 @@ models:
       Not that inference efficiency is not important, we will address that subsequently.)
     language: ["multilingual"]
     repository: "https://huggingface.co/prithivida/Splade_PP_en_v2"
+
+  granite-30m:
+    name: "ibm-granite/granite-embedding-30m-sparse"
+    type: "sparse-embeddings"
+    dimension: 1234   # must add this field
+    max_tokens: 1234
+    description: |
+       Granite-Embedding-30m-Sparse is a 30M parameter sparse biencoder embedding model from the Granite Experimental suite that can be used to generate high quality text embeddings. 
+       This model produces variable length bag-of-word like dictionary, containing expansions of sentence tokens and their corresponding weights and is trained using a combination of open source relevance-pair datasets with permissive,
+       enterprise-friendly license, and IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. 
+       This model is developed using retrieval oriented pretraining, contrastive finetuning and knowledge distillation for improved performance.
+    language: ["En"]
+    repository: "https://huggingface.co/ibm-granite/granite-embedding-30m-sparse"