Text Classification
Transformers
English
tenstorrent
blackhole
p300x2
vllm-plugin
nli
cross-encoder
reranker
qwen3.5
Instructions to use changh95/openjev-p300x2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use changh95/openjev-p300x2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="changh95/openjev-p300x2")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("changh95/openjev-p300x2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scripts/run_classify_test.sh from changh95/openjev-p300x2: direct link, hf CLI and curl.
- Browser
- Download file 238 Bytes
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https://huggingface.co/changh95/openjev-p300x2/resolve/main/scripts/run_classify_test.sh
- Command line
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hf download hf://changh95/openjev-p300x2/scripts/run_classify_test.sh
-
curl -L -o run_classify_test.sh https://huggingface.co/changh95/openjev-p300x2/resolve/main/scripts/run_classify_test.sh
238 Bytes
| # Device test: TP=4 classification vs CPU reference. | |
| source /home/ttuser/experiments/openjev/env.sh | |
| cd $TT_METAL_HOME | |
| exec $PYTHON_ENV_DIR/bin/python -m pytest models/demos/blackhole/qwen36/tests/test_classify_tp.py -svq "$@" | |