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
File size: 238 Bytes
32bed67 | 1 2 3 4 5 6 | #!/bin/bash
# Device test: TP=4 classification vs CPU reference.
source /home/ttuser/experiments/openjev/env.sh
cd $TT_METAL_HOME
exec $PYTHON_ENV_DIR/bin/python -m pytest models/demos/blackhole/qwen36/tests/test_classify_tp.py -svq "$@"
|