Question Answering
Transformers
Safetensors
English
qwen3
text-generation
Pathology
Agent
text-generation-inference
Instructions to use WenchuanZhang/Agentic-Router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WenchuanZhang/Agentic-Router with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="WenchuanZhang/Agentic-Router")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("WenchuanZhang/Agentic-Router") model = AutoModelForCausalLM.from_pretrained("WenchuanZhang/Agentic-Router", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from WenchuanZhang/Agentic-Router: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/WenchuanZhang/Agentic-Router/resolve/main/tokenizer.json
- Command line
-
hf download hf://WenchuanZhang/Agentic-Router/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/WenchuanZhang/Agentic-Router/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 693ec4b3922b0bd306bf7b4989e115ffbfeb7b0c08b31bc6d956818c6bb07f61
- Size of remote file:
- 11.4 MB
- SHA256:
- aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
路
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