Text Classification
Transformers
PyTorch
English
Chinese
internlm2
feature-extraction
Reward
RL
RFT
Reward Model
custom_code
Instructions to use internlm/POLAR-1_8B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/POLAR-1_8B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="internlm/POLAR-1_8B-Base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("internlm/POLAR-1_8B-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from internlm/POLAR-1_8B-Base: direct link, hf CLI and curl.
- Browser
- Download file 10.5 MB
-
https://huggingface.co/internlm/POLAR-1_8B-Base/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://internlm/POLAR-1_8B-Base@refs/pr/2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/internlm/POLAR-1_8B-Base/resolve/refs%2Fpr%2F2/tokenizer.json
10.5 MB
- Xet hash:
- c0453db946bb0a6a3c4cf9fd71c3fdd1ef2bd4a294033236f3be3195a76fc0cc
- Size of remote file:
- 10.5 MB
- SHA256:
- 20286a7a382e842fe9fbbee3113a916fb407c69320f97fc1dc07ab3a7985db5f
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