Text Classification
Transformers
Safetensors
English
Chinese
qwen3_5_moe_text
text-generation
decision-model
web-agent
browser-agent
typed-decisions
structured-output
one-pass
mixture-of-experts
Instructions to use Lexmount/WebJev-35B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lexmount/WebJev-35B-A3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Lexmount/WebJev-35B-A3B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Lexmount/WebJev-35B-A3B") model = AutoModelForCausalLM.from_pretrained("Lexmount/WebJev-35B-A3B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/liveweb.png from Lexmount/WebJev-35B-A3B: direct link, hf CLI and curl.
- Browser
- Download file 97.5 kB
-
https://huggingface.co/Lexmount/WebJev-35B-A3B/resolve/main/assets/liveweb.png
- Command line
-
hf download hf://Lexmount/WebJev-35B-A3B/assets/liveweb.png
-
curl -L -o liveweb.png https://huggingface.co/Lexmount/WebJev-35B-A3B/resolve/main/assets/liveweb.png
97.5 kB

- Xet hash:
- 243e04f591cd306f68a2124cfacc96f4b29cecff3edd3d2de59a0aaccadb1c0a
- Size of remote file:
- 97.5 kB
- SHA256:
- dedd9a5140c1019cd6c43a7e82e079e3d7598e8c3ced7e52bb628babe5db6459
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