Feature Extraction
Transformers
Safetensors
Laya
English
multilingual
laya_browser
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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cklxx/laya-browser", trust_remote_code=True, device_map="auto") - Laya
How to use cklxx/laya-browser with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 1,319 Bytes
adf912b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | import os, sys, json, time
sys.path.insert(0, "/home/ckl/projects/S/jev-ultrafast")
os.environ.update(BU_CDP_URL="http://127.0.0.1:9222", TYPESAFE_BASE_URL="http://127.0.0.1:8791", TYPESAFE_API_KEY="local",
TEXT_MODEL_API_KEY="local", TEXT_MODEL_BASE_URL="http://127.0.0.1:30000/v1", TEXT_MODEL="Qwen/Qwen3-4B-AWQ",
TEXT_MODEL_EXTRA_JSON='{"chat_template_kwargs": {"enable_thinking": false}}')
from jev_ultrafast import Agent
url = sys.argv[1]; goal = sys.argv[2]
t = time.time()
with Agent(url, goal) as agent:
print("elements on first page:", len(agent.snapshot()["elements"]), "| title:", agent.state["page"]["title"])
for state in agent.run():
h = state["history"][-1] if state["history"] else None
d = state["decisions"][-1] if state["decisions"] else None
print(f"{state['elapsed_ms']:6d} ms status={state['status']:9s} op={d['operation'] if d else None:9s} "
f"conf={d['confidence'] if d else 0:.2f} model={d['latency_ms'] if d else 0:4d}ms "
f"action={h['action'][:60] if h else None} text={h['text'] if h else None}")
if len(state["history"]) > 25: break
print("FINAL:", state["status"], "| url:", state["page"]["url"], "| title:", state["page"]["title"], "| wall", round(time.time() - t, 1), "s")
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