Add/update the quantized ONNX model files and README.md for Transformers.js v3
Browse files## Applied Quantizations
### β
Based on `vision_model.onnx` *with* slimming
β³ β
`int8`: `vision_model_int8.onnx` (added)
β³ β
`uint8`: `vision_model_uint8.onnx` (added)
β³ β
`q4`: `vision_model_q4.onnx` (added)
β³ β
`q4f16`: `vision_model_q4f16.onnx` (added)
β³ β
`bnb4`: `vision_model_bnb4.onnx` (added)
### β
Based on `vision_model.onnx` *with* slimming
β³ β
`int8`: `vision_model_int8.onnx` (added)
β³ β
`uint8`: `vision_model_uint8.onnx` (added)
β³ β
`q4`: `vision_model_q4.onnx` (added)
β³ β
`q4f16`: `vision_model_q4f16.onnx` (added)
β³ β
`bnb4`: `vision_model_bnb4.onnx` (added)
### β Based on `model.onnx` *with* slimming
```
None
```
β³ β `int8`: `model_int8.onnx` (added but JS-based E2E test failed)
```
/home/ubuntu/src/tjsmigration/node_modules/.pnpm/[email protected]/node_modules/onnxruntime-node/dist/backend.js:25
__classPrivateFieldGet(this, _OnnxruntimeSessionHandler_inferenceSession, "f").loadModel(pathOrBuffer, options);
^
Error: Could not find an implementation for ConvInteger(10) node with name '/vision_model/embeddings/patch_embedding/Conv_quant'
at new OnnxruntimeSessionHandler (/home/ubuntu/src/tjsmigration/node_modules/.pnpm/[email protected]/node_modules/onnxruntime-node/dist/backend.js:25:92)
at Immediate.<anonymous> (/home/ubuntu/src/tjsmigration/node_modules/.pnpm/[email protected]/node_modules/onnxruntime-node/dist/backend.js:67:29)
at process.processImmediate (node:internal/timers:485:21)
Node.js v22.16.0
```
β³ β
`uint8`: `model_uint8.onnx` (added)
β³ β
`q4`: `model_q4.onnx` (added)
β³ β
`q4f16`: `model_q4f16.onnx` (added)
β³ β
`bnb4`: `model_bnb4.onnx` (added)
### β Based on `model.onnx` *with* slimming
```
None
```
β³ β `int8`: `model_int8.onnx` (added but JS-based E2E test failed)
```
/home/ubuntu/src/tjsmigration/node_modules/.pnpm/[email protected]/node_modules/onnxruntime-node/dist/backend.js:25
__classPrivateFieldGet(this, _OnnxruntimeSessionHandler_inferenceSession, "f").loadModel(pathOrBuffer, options);
^
Error: Could not find an implementation for ConvInteger(10) node with name '/vision_model/embeddings/patch_embedding/Conv_quant'
at new OnnxruntimeSessionHandler (/home/ubuntu/src/tjsmigration/node_modules/.pnpm/[email protected]/node_modules/onnxruntime-node/dist/backend.js:25:92)
at Immediate.<anonymous> (/home/ubuntu/src/tjsmigration/node_modules/.pnpm/[email protected]/node_modules/onnxruntime-node/dist/backend.js:67:29)
at process.processImmediate (node:internal/timers:485:21)
Node.js v22.16.0
```
β³ β
`uint8`: `model_uint8.onnx` (added)
β³ β
`q4`: `model_q4.onnx` (added)
β³ β
`q4f16`: `model_q4f16.onnx` (added)
β³ β
`bnb4`: `model_bnb4.onnx` (added)
### β
Based on `text_model.onnx` *with* slimming
β³ β
`int8`: `text_model_int8.onnx` (added)
β³ β
`uint8`: `text_model_uint8.onnx` (added)
β³ β
`q4`: `text_model_q4.onnx` (added)
β³ β
`q4f16`: `text_model_q4f16.onnx` (added)
β³ β
`bnb4`: `text_model_bnb4.onnx` (added)
### β
Based on `text_model.onnx` *with* slimming
β³ β
`int8`: `text_model_int8.onnx` (added)
β³ β
`uint8`: `text_model_uint8.onnx` (added)
β³ β
`q4`: `text_model_q4.onnx` (added)
β³ β
`q4f16`: `text_model_q4f16.onnx` (added)
β³ β
`bnb4`: `text_model_bnb4.onnx` (added)
- README.md +5 -5
- onnx/model_bnb4.onnx +3 -0
- onnx/model_q4.onnx +3 -0
- onnx/model_q4f16.onnx +3 -0
- onnx/model_uint8.onnx +3 -0
- onnx/text_model_bnb4.onnx +3 -0
- onnx/text_model_int8.onnx +3 -0
- onnx/text_model_q4.onnx +3 -0
- onnx/text_model_q4f16.onnx +3 -0
- onnx/text_model_uint8.onnx +3 -0
- onnx/vision_model_bnb4.onnx +3 -0
- onnx/vision_model_int8.onnx +3 -0
- onnx/vision_model_q4.onnx +3 -0
- onnx/vision_model_q4f16.onnx +3 -0
- onnx/vision_model_uint8.onnx +3 -0
@@ -7,14 +7,14 @@ https://huggingface.co/google/siglip-base-patch16-384 with ONNX weights to be co
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## Usage (Transformers.js)
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-
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@
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```bash
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-
npm i @
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```
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**Example:** Zero-shot image classification w/ `Xenova/siglip-base-patch16-384`:
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```js
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import { pipeline } from '@
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const classifier = await pipeline('zero-shot-image-classification', 'Xenova/siglip-base-patch16-384');
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const url = 'http://images.cocodataset.org/val2017/000000039769.jpg';
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@@ -31,7 +31,7 @@ console.log(output);
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**Example:** Compute text embeddings with `SiglipTextModel`.
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```javascript
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import { AutoTokenizer, SiglipTextModel } from '@
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// Load tokenizer and text model
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const tokenizer = await AutoTokenizer.from_pretrained('Xenova/siglip-base-patch16-384');
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**Example:** Compute vision embeddings with `SiglipVisionModel`.
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```javascript
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-
import { AutoProcessor, SiglipVisionModel, RawImage} from '@
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// Load processor and vision model
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const processor = await AutoProcessor.from_pretrained('Xenova/siglip-base-patch16-384');
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## Usage (Transformers.js)
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+
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
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```bash
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+
npm i @huggingface/transformers
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```
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**Example:** Zero-shot image classification w/ `Xenova/siglip-base-patch16-384`:
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```js
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+
import { pipeline } from '@huggingface/transformers';
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const classifier = await pipeline('zero-shot-image-classification', 'Xenova/siglip-base-patch16-384');
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const url = 'http://images.cocodataset.org/val2017/000000039769.jpg';
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**Example:** Compute text embeddings with `SiglipTextModel`.
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```javascript
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+
import { AutoTokenizer, SiglipTextModel } from '@huggingface/transformers';
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// Load tokenizer and text model
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const tokenizer = await AutoTokenizer.from_pretrained('Xenova/siglip-base-patch16-384');
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**Example:** Compute vision embeddings with `SiglipVisionModel`.
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```javascript
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+
import { AutoProcessor, SiglipVisionModel, RawImage} from '@huggingface/transformers';
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// Load processor and vision model
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const processor = await AutoProcessor.from_pretrained('Xenova/siglip-base-patch16-384');
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