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: 817 Bytes
454b3e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | """Fixed random subset of an items file.
python finetune/make_subset.py <items.pt> <out.pt> [n=100000] [seed=0] [exclude_src=a,b]"""
import random, sys, torch
items = torch.load(sys.argv[1], weights_only=False)
excl = set(sys.argv[5].split(",")) if len(sys.argv) > 5 and sys.argv[5] else set()
pool = [i for i in range(len(items)) if items[i].get("src") not in excl]
n = min(int(sys.argv[3]) if len(sys.argv) > 3 else 100000, len(pool))
idx = sorted(random.Random(int(sys.argv[4]) if len(sys.argv) > 4 else 0).sample(pool, n))
sub = [items[i] for i in idx]
torch.save(sub, sys.argv[2])
import collections
print(len(items), "->", len(sub), "src", dict(collections.Counter(i.get("src") for i in sub).most_common(8)),
"goal_done", dict(collections.Counter(i["label"] for i in sub if i["qid"] == "goal_done")))
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