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
Download code/sweep_flappy.sh from ldov/openjevv: direct link, hf CLI and curl.
- Browser
- Download file 968 Bytes
-
https://huggingface.co/ldov/openjevv/resolve/main/code/sweep_flappy.sh
- Command line
-
hf download hf://ldov/openjevv/code/sweep_flappy.sh
-
curl -L -o sweep_flappy.sh https://huggingface.co/ldov/openjevv/resolve/main/code/sweep_flappy.sh
968 Bytes
| # 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 | |