Text Classification
Transformers
PyTorch
English
Chinese
internlm2
feature-extraction
Reward
RL
RFT
Reward Model
custom_code
Instructions to use internlm/POLAR-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/POLAR-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="internlm/POLAR-7B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("internlm/POLAR-7B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from internlm/POLAR-7B: direct link, hf CLI and curl.
- Browser
- Download file 10.5 MB
-
https://huggingface.co/internlm/POLAR-7B/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://internlm/POLAR-7B@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/internlm/POLAR-7B/resolve/refs%2Fpr%2F1/tokenizer.json
10.5 MB
- Xet hash:
- 1f6bcc35083dc899d5fcdded03ff54215854dc840e7f1cb261ae9d6b1601ef34
- Size of remote file:
- 10.5 MB
- SHA256:
- 70a943188e9095abfad4aaa538c549de1b87aa819af4367904c18d9b9c802291
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