Update README.md

README.md

@@ -45,7 +45,7 @@ pipeline_tag: sentence-similarity
 
 - [License and Terms:](#license-and-terms)
 - [Detailed comparison & Our Contribution:](#detailed-comparison--our-contribution)
-- [ONNX & GGUF Variants:](#detailed-comparison--our-contribution)
+- [ONNX & GGUF Status:](#onnx-gguf-status)
 - [Usage:](#usage)
     - [With Sentence Transformers:](#with-sentence-transformers)
     - [With Huggingface Transformers:](#with-huggingface-transformers)
@@ -91,6 +91,13 @@ Full set of evaluation numbers for our model
 
 <br/>
 
+# ONNX & GGUF Status:
+
+|Variant| Status |
+|:---:|:---:|
+|FP16 ONNX | ✅ |
+|GGUF | WIP| 
+
 # Usage:
 
 #### With Sentence Transformers:
@@ -167,7 +174,7 @@ The below numbers are with mDPR model, but miniMiracle_zh_v1 should give a even
 *Note: MIRACL paper shows a different (higher) value for BM25 Chinese, So we are taking that value from BGE-M3 paper, rest all are form the MIRACL paper.*
 
 #### cMTEB numbers:
-CMTEB is a general purpose embedding evaluation bechmark covering wide range of tasks, but like BGE-M3, miniMiracle models are predominantly tuned for retireval tasks aimed at search & IR based usecases.
+CMTEB is a general purpose embedding evaluation benchmark covering wide range of tasks, but like BGE-M3, miniMiracle models are predominantly tuned for retireval tasks aimed at search & IR based usecases.
 We ran the retrieval slice of the cMTEB and add the scores here.
 
 We compared the performance few top general purpose embedding models on the C-MTEB benchmark. please refer to the C-MTEB leaderboard.