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 generation_config.json from Lexmount/WebJev-35B-A3B: direct link, hf CLI and curl.
- Browser
- Download file 116 Bytes
-
https://huggingface.co/Lexmount/WebJev-35B-A3B/resolve/844d4f7aba90058743a5deeaa2e7fe80921028d8/generation_config.json
- Command line
-
hf download hf://Lexmount/WebJev-35B-A3B@844d4f7aba90058743a5deeaa2e7fe80921028d8/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/Lexmount/WebJev-35B-A3B/resolve/844d4f7aba90058743a5deeaa2e7fe80921028d8/generation_config.json
116 Bytes
| { | |
| "_from_model_config": true, | |
| "eos_token_id": 248044, | |
| "transformers_version": "5.15.1", | |
| "use_cache": true | |
| } | |