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README.md

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+---
+license: apache-2.0
+base_model: jinaai/jina-embeddings-v2-base-code
+tags:
+  - onnx
+  - int8
+  - quantized
+  - code-embeddings
+  - sentence-transformers
+library_name: onnxruntime
+pipeline_tag: feature-extraction
+---
+
+# jina-embeddings-v2-base-code (INT8 Quantized)
+
+INT8 dynamically quantized version of [jinaai/jina-embeddings-v2-base-code](https://huggingface.co/jinaai/jina-embeddings-v2-base-code) for efficient CPU inference.
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Base Model | jinaai/jina-embeddings-v2-base-code |
+| Quantization | INT8 (dynamic) |
+| Size | 154 MB (vs 612 MB fp32) |
+| Dimensions | 768 |
+| Max Tokens | 8192 |
+| Languages | English + 30 programming languages |
+
+## Usage
+
+```python
+import onnxruntime as ort
+from huggingface_hub import hf_hub_download
+from tokenizers import Tokenizer
+import numpy as np
+
+# Load
+tokenizer = Tokenizer.from_file(hf_hub_download("nijaru/jina-code-int8", "tokenizer.json"))
+tokenizer.enable_padding(pad_id=0, pad_token="[PAD]")
+tokenizer.enable_truncation(max_length=512)
+session = ort.InferenceSession(hf_hub_download("nijaru/jina-code-int8", "model_int8.onnx"))
+
+def embed(texts):
+    encoded = tokenizer.encode_batch(texts)
+    input_ids = np.array([e.ids for e in encoded], dtype=np.int64)
+    attention_mask = np.array([e.attention_mask for e in encoded], dtype=np.int64)
+    outputs = session.run(None, {"input_ids": input_ids, "attention_mask": attention_mask})
+    embeddings = outputs[0]
+    mask = attention_mask[:, :, np.newaxis]
+    return (embeddings * mask).sum(axis=1) / mask.sum(axis=1)
+
+embeddings = embed(["def hello(): pass", "authentication flow"])
+```
+
+## License
+
+Apache-2.0 (same as base model)
+
+## Attribution
+
+Quantized from [jinaai/jina-embeddings-v2-base-code](https://huggingface.co/jinaai/jina-embeddings-v2-base-code) by Jina AI.

model_int8.onnx

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