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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_optimized.onnx.data filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,57 @@
+---
+license: mit
+pipeline_tag: feature-extraction
+---
+
+# bge-m3-onnx-o4
+
+This is `bge-m3-onnx-o4` weights of the original [`BAAI/bge-m3`](https://huggingface.co/BAAI/bge-m3). Why is this model cool?
+
+- [x] Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.
+- [x] Multi-Linguality: It can support more than **100** working languages.
+- [x] Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to **8192** tokens.
+
+## Usage
+
+### Dense Retrieval
+
+```
+# for cuda 
+pip install --upgrade-strategy eager optimum[onnxruntime]
+```
+
+```python
+
+from optimum.onnxruntime import ORTModelForFeatureExtraction
+from transformers import AutoTokenizer
+import torch
+
+model = ORTModelForFeatureExtraction.from_pretrained("hooman650/bge-m3-onnx-o4", provider="CUDAExecutionProvider")
+tokenizer = AutoTokenizer.from_pretrained("hooman650/bge-m3-onnx-o4")
+
+sentences = [
+    "English: The quick brown fox jumps over the lazy dog.",
+    "Spanish: El rápido zorro marrón salta sobre el perro perezoso.",
+    "French: Le renard brun rapide saute par-dessus le chien paresseux.",
+    "German: Der schnelle braune Fuchs springt über den faulen Hund.",
+    "Italian: La volpe marrone veloce salta sopra il cane pigro.",
+    "Japanese: 速い茶色の狐が怠惰な犬を飛び越える。",
+    "Chinese (Simplified): 快速的棕色狐狸跳过懒狗。",
+    "Russian: Быстрая коричневая лиса прыгает через ленивую собаку.",
+    "Arabic: الثعلب البني السريع يقفز فوق الكلب الكسول.",
+    "Hindi: तेज़ भूरी लोमड़ी आलसी कुत्ते के ऊपर कूद जाती है।"
+]
+
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt').to("cuda")
+
+# Get the embeddings
+out=model(**encoded_input,return_dict=True).last_hidden_state
+
+# normalize the embeddings
+dense_vecs = torch.nn.functional.normalize(out[:, 0], dim=-1)
+```
+### Multi-Vector (ColBERT)
+
+`coming soon...`
+
+

README.md

@@ -1,3 +1,57 @@
 ---
 license: mit
+pipeline_tag: feature-extraction
 ---
+
+# bge-m3-onnx-o4
+
+This is `bge-m3-onnx-o4` weights of the original [`BAAI/bge-m3`](https://huggingface.co/BAAI/bge-m3). Why is this model cool?
+
+- [x] Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.
+- [x] Multi-Linguality: It can support more than **100** working languages.
+- [x] Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to **8192** tokens.
+
+## Usage
+
+### Dense Retrieval
+
+```
+# for cuda 
+pip install --upgrade-strategy eager optimum[onnxruntime]
+```
+
+```python
+
+from optimum.onnxruntime import ORTModelForFeatureExtraction
+from transformers import AutoTokenizer
+import torch
+
+model = ORTModelForFeatureExtraction.from_pretrained("hooman650/bge-m3-onnx-o4", provider="CUDAExecutionProvider")
+tokenizer = AutoTokenizer.from_pretrained("hooman650/bge-m3-onnx-o4")
+
+sentences = [
+    "English: The quick brown fox jumps over the lazy dog.",
+    "Spanish: El rápido zorro marrón salta sobre el perro perezoso.",
+    "French: Le renard brun rapide saute par-dessus le chien paresseux.",
+    "German: Der schnelle braune Fuchs springt über den faulen Hund.",
+    "Italian: La volpe marrone veloce salta sopra il cane pigro.",
+    "Japanese: 速い茶色の狐が怠惰な犬を飛び越える。",
+    "Chinese (Simplified): 快速的棕色狐狸跳过懒狗。",
+    "Russian: Быстрая коричневая лиса прыгает через ленивую собаку.",
+    "Arabic: الثعلب البني السريع يقفز فوق الكلب الكسول.",
+    "Hindi: तेज़ भूरी लोमड़ी आलसी कुत्ते के ऊपर कूद जाती है।"
+]
+
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt').to("cuda")
+
+# Get the embeddings
+out=model(**encoded_input,return_dict=True).last_hidden_state
+
+# normalize the embeddings
+dense_vecs = torch.nn.functional.normalize(out[:, 0], dim=-1)
+```
+### Multi-Vector (ColBERT)
+
+`coming soon...`
+
+

config.json

@@ -0,0 +1,28 @@
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+  ],
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+  "initializer_range": 0.02,
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+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

model_optimized.onnx

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model_optimized.onnx.data

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ort_config.json

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+    "enable_gemm_fast_gelu_fusion": false,
+    "enable_transformers_specific_optimizations": true,
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+    "optimization_level": 2,
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+    "use_multi_head_attention": false,
+    "use_raw_attention_mask": false
+  },
+  "optimum_version": "1.16.2",
+  "quantization": {},
+  "transformers_version": "4.37.2",
+  "use_external_data_format": true
+}

sentencepiece.bpe.model

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special_tokens_map.json

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+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+}

tokenizer.json

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tokenizer_config.json

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+}