Feature Extraction
Transformers
Safetensors
English
multilingual
laya_browser
laya
custom_code
system-1
browser-agent
web-navigation
decision-model
mmbert
mind2web
tilelang
Instructions to use cklxx/laya-browser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cklxx/laya-browser with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cklxx/laya-browser", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cklxx/laya-browser", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,161 Bytes
adf912b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | #!/bin/bash
# build items -> zero-shot baselines -> fine-tune -> eval. Logs to finetune/out/run.log
cd /home/ckl/projects/S/laya && source env.sh
export LAYA_BASE=$(ls -d ~/.cache/huggingface/hub/models--convaiinnovations--laya/snapshots/*/typed-decisions | head -1)
P=.venv/bin/python
for p in $(pgrep -f "sglang.launch_server"); do kill $p; done; sleep 3
uv pip install --python $P "httpx[http2]" 2>&1 | tail -1
set -e
echo "== build items"; $P finetune/build_items.py finetune/out/pages.jsonl finetune/out/cases.jsonl finetune/out/
echo "== zero-shot baselines"
$P finetune/eval.py finetune/out/pages.jsonl finetune/out/eval_cases.jsonl "$LAYA_BASE" 2>&1 | grep -vE "TileLang|Warn|warn|Fetch"
$P finetune/eval.py finetune/out/pages.jsonl finetune/out/eval_cases.jsonl convaiinnovations/laya multilingual 2>&1 | grep -vE "TileLang|Warn|warn|Fetch"
echo "== train"; $P finetune/train.py finetune/out/train_items.pt finetune/out/laya-browser 4 2>&1 | grep -vE "Warn|warn"
echo "== eval fine-tuned"
$P finetune/eval.py finetune/out/pages.jsonl finetune/out/eval_cases.jsonl finetune/out/laya-browser 2>&1 | grep -vE "TileLang|Warn|warn|Fetch"
echo RUN_ALL_DONE
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