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: 492 Bytes
454b3e6 | 1 2 3 4 5 6 7 8 9 10 11 12 | operation label counts: {'CLICK': 919}
train items 2118 (op 919, target 1199), eval cases 0, skipped 0, seq len mean 777 max 1024
x6c items: 30000 replay + 2118 x3 contrast = 36354
== train x6c
=== epoch 1/1 avg loss 0.6976 1301s
saved finetune/out/laya-browser-x6c
== laya-browser-x6c_laya-browser-x6c suite C x2
== 12/54 passed (22%, 2 run(s) per task) | median wall 14.8s
== laya-browser-x6_laya-browser-x6 suite C x2
== 14/54 passed (26%, 2 run(s) per task) | median wall 13.9s
CTR_DONE
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