| --- |
| license: apache-2.0 |
| base_model: knowledgator/gliclass-v4-edge |
| library_name: onnx |
| tags: [onnx, webgpu, game, zero-shot-classification] |
| --- |
| |
| # Handler: GLiClass for orders |
|
|
| knowledgator/gliclass-v4-edge (built on microsoft/deberta-v3-large) fine-tuned to read the spoken orders in Handler, |
| a game in which you talk two spies through a mission. These are the ONNX graphs the game runs in the browser with |
| onnxruntime-web on WebGPU, one run per question: |
|
|
| - `onnx/model_q8f16.onnx` (what the game loads): 8-bit weights (MatMulNBits, weight-only, activations fp16), and the |
| word embeddings cut out: it takes `inputs_embeds` (fp16, [batch, length, 1024]) beside `input_ids`, looked up from |
| `onnx/embed_tokens_fp16.bin` (128,004 rows of 1024 fp16 values, row = token id). |
| - `onnx/model_fp16.onnx`: the fp16 export it was made from, taking `input_ids` alone. |
| - Inputs `input_ids` and `attention_mask` (plus `inputs_embeds` for q8f16); the output is calibrated logits for 25 |
| option slots. Keep the first n for a question's n options and apply `calibration.json`'s temperature: softmax(logits / T). |
| - The text is every option as `<<LABEL>>key: description`, then `<<SEP>>`, the question's instructions and the |
| state (gliclass 0.1.22's uni-encoder pipeline). |
|
|
| Licence: Apache 2.0, as for GLiClass v4 Edge (Knowledgator); DeBERTa-v3 by Microsoft is MIT. |
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