Text Classification
Transformers
Safetensors
English
nli
cross-encoder
qwen3.5
reranker
image-text-to-text
Instructions to use ldov/openjevv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ldov/openjevv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ldov/openjevv")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ldov/openjevv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 968 Bytes
e46c127 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | #!/usr/bin/env bash
# 11 Flappy variants (prompts / hypothesis phrasing / label & training params / backbone), as two parallel chains.
# usage: bash sweep_flappy.sh
cd ~/qwen_nli
P=${PY:-python}
COMMON="--episodes 6 --fps 15 --max-steps 900 --record-only --seed 1"
run() { name=$1; gpu=$2; shift 2; HF_HOME=/mnt/hf CUDA_VISIBLE_DEVICES=$gpu $P flappy.py $COMMON --out results/sweep/$name.json "$@" > logs/sweep_$name.log 2>&1; }
mkdir -p results/sweep logs
(
run v00_base 0
run v01_numeric 0 --prompt numeric
run v02_coach 0 --prompt coach
run v03_ascii 0 --prompt ascii
run v04_hyp_should 0 --hyp should
run v05_coach_should 0 --prompt coach --hyp should
) &
(
run v06_lookahead6 1 --lookahead 6 --skip-nli
run v07_noise0.3 1 --noise 0.3 --skip-nli
run v08_data200 1 --collect-episodes 200 --skip-nli
run v09_eps0.3 1 --eps 0.3 --skip-nli
run v10_raw4b 1 --ckpt Qwen/Qwen3.5-4B
) &
wait
echo SWEEP_DONE
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