Token Classification
Transformers
ONNX
Safetensors
multilingual
bert
autofill
password-manager
form-classification
field-classification
browser-extension
Instructions to use ProCreations/Zap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProCreations/Zap with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ProCreations/Zap")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ProCreations/Zap") model = AutoModelForTokenClassification.from_pretrained("ProCreations/Zap", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training/runner/t00_setup.sh from ProCreations/Zap: direct link, hf CLI and curl.
- Browser
- Download file 1.36 kB
-
https://huggingface.co/ProCreations/Zap/resolve/main/training/runner/t00_setup.sh
- Command line
-
hf download hf://ProCreations/Zap/training/runner/t00_setup.sh
-
curl -L -o t00_setup.sh https://huggingface.co/ProCreations/Zap/resolve/main/training/runner/t00_setup.sh
1.36 kB
| set -x | |
| cd /job/work | |
| python3 -m venv --system-site-packages /job/work/.venv | |
| . /job/work/.venv/bin/activate | |
| pip install -q selectolax warcio onnx onnxruntime onnxscript tldextract aiohttp 2>&1 | tail -3 | |
| python3 -c "import selectolax, onnx, onnxruntime, aiohttp, torch; print('ok', torch.cuda.is_available())" | |
| # compare baked overlays in image vs production overlay files | |
| P=/home/user/.local/share/rtx-pro-apps/qwen3.8-flash-next/runtime-speed-20260924 | |
| for pair in "qwen_sparse_attn_backend.py:layers/attention/qwen_sparse_attn_backend.py" "qsa_sparse_attn.py:layers/attention/qsa/sparse_attn.py" "qwen4_exp_mtp_hotmap.py:models/qwen4_exp_mtp.py" "eagle_worker_v2.py:speculative/eagle_worker_v2.py" "metadata.py:layers/attention/qsa/metadata.py" "graph_metadata.py:layers/attention/qsa/graph_metadata.py" "qsa_indexer.py:layers/attention/qsa/qsa_indexer.py" "qsa_kv_pool.py:mem_cache/qsa_kv_pool.py" "eagle_utils.py:speculative/eagle_utils.py" "qwenfast.py:qwenfast.py" "qwen4_exp_startup.py:models/qwen4_exp.py" "loader_startup.py:model_loader/loader.py"; do | |
| a=${pair%%:*}; b=${pair#*:} | |
| echo "$a $(sha256sum $P/$a | cut -c1-12) $(sha256sum /sgl-workspace/sglang/python/sglang/srt/$b | cut -c1-12)" | |
| done | |
| ls /opt/ | head; ls /opt/qwen* 2>/dev/null | |
| curl -sI https://data.commoncrawl.org/crawl-data/CC-MAIN-2026-39/warc.paths.gz | head -3 | |
| df -h /job | tail -1 | |