Text Classification
Transformers
Safetensors
decision-model
classification
julia
open-jev
head-finetune
low-resource
Instructions to use SHSLab/Qyvos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SHSLab/Qyvos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SHSLab/Qyvos")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SHSLab/Qyvos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Qyvos v1: Julia-1 backbone (bit-exact) + Open-Jev head fine-tune (30k rows, low-RAM protocol)
31f7037 verified Download julia/router/native/PROOF.bend from SHSLab/Qyvos: direct link, hf CLI and curl.
- Browser
- Download file 2.35 kB
-
https://huggingface.co/SHSLab/Qyvos/resolve/main/julia/router/native/PROOF.bend
- Command line
-
hf download hf://SHSLab/Qyvos/julia/router/native/PROOF.bend
-
curl -L -o PROOF.bend https://huggingface.co/SHSLab/Qyvos/resolve/main/julia/router/native/PROOF.bend
2.35 kB
| import Base | |
| import ./router.bend as R | |
| import ./LAWS.bend as Laws | |
| def Laws.dot_tile_shape(xs, ws, s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12, s13, s14, s15, s16, s17, s18, s19, s20, s21, s22, s23, s24, s25, s26, s27): | |
| match xs ws: | |
| case R.JTVal{+x0, +x1, +x2, +x3, xt} R.JWVal{+w0, +w1, +w2, +w3, +w4, +w5, +w6, wt}: | |
| Laws.dot_tile_shape(xt, wt, F32.add(s0, F32.mul(x0, w0)), | |
| F32.add(s1, F32.mul(x0, w1)), | |
| F32.add(s2, F32.mul(x0, w2)), | |
| F32.add(s3, F32.mul(x0, w3)), | |
| F32.add(s4, F32.mul(x0, w4)), | |
| F32.add(s5, F32.mul(x0, w5)), | |
| F32.add(s6, F32.mul(x0, w6)), | |
| F32.add(s7, F32.mul(x1, w0)), | |
| F32.add(s8, F32.mul(x1, w1)), | |
| F32.add(s9, F32.mul(x1, w2)), | |
| F32.add(s10, F32.mul(x1, w3)), | |
| F32.add(s11, F32.mul(x1, w4)), | |
| F32.add(s12, F32.mul(x1, w5)), | |
| F32.add(s13, F32.mul(x1, w6)), | |
| F32.add(s14, F32.mul(x2, w0)), | |
| F32.add(s15, F32.mul(x2, w1)), | |
| F32.add(s16, F32.mul(x2, w2)), | |
| F32.add(s17, F32.mul(x2, w3)), | |
| F32.add(s18, F32.mul(x2, w4)), | |
| F32.add(s19, F32.mul(x2, w5)), | |
| F32.add(s20, F32.mul(x2, w6)), | |
| F32.add(s21, F32.mul(x3, w0)), | |
| F32.add(s22, F32.mul(x3, w1)), | |
| F32.add(s23, F32.mul(x3, w2)), | |
| F32.add(s24, F32.mul(x3, w3)), | |
| F32.add(s25, F32.mul(x3, w4)), | |
| F32.add(s26, F32.mul(x3, w5)), | |
| F32.add(s27, F32.mul(x3, w6))) | |
| case R.JTVal{_, _, _, _, _} R.JWEnd{}: | |
| {==} | |
| case R.JTEnd{} R.JWVal{_, _, _, _, _, _, _, _}: | |
| {==} | |
| case R.JTEnd{} R.JWEnd{}: | |
| {==} | |
| def Laws.dense_shape(xs, weights): | |
| match weights: | |
| case R.JMLeaf{ws}: | |
| Laws.dot_tile_shape(xs, ws, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | |
| case R.JMFork{+left, +right}: | |
| %Laws.dense_shape(xs, left) : {Nat.add(Laws.output_tiles(R.dense_tile(xs, left)), Laws.output_tiles(R.dense_tile(xs, right))) == Nat.add(_, Laws.matrix_tiles(right)) : Nat} | |
| %Laws.dense_shape(xs, right) : {Nat.add(Laws.output_tiles(R.dense_tile(xs, left)), Laws.output_tiles(R.dense_tile(xs, right))) == Nat.add(Laws.output_tiles(R.dense_tile(xs, left)), _) : Nat} | |
| {==} | |