Instructions to use opencompass/anah-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use opencompass/anah-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="opencompass/anah-v2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("opencompass/anah-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from opencompass/anah-v2: direct link, hf CLI and curl.
- Browser
- Download file 5.75 MB
-
https://huggingface.co/opencompass/anah-v2/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://opencompass/anah-v2@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/opencompass/anah-v2/resolve/refs%2Fpr%2F1/tokenizer.json
5.75 MB
- Xet hash:
- 09ca924075d1f99570d86c401f4d2712aa1c3670469e61004f80e293ca9cdbad
- Size of remote file:
- 5.75 MB
- SHA256:
- 9c1f649fe0ac36a053b6beca3ac1c0a170ec1f2b4d99acda1e4ffc78715a7bf1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.