Text Classification
Transformers
PyTorch
English
Chinese
internlm2
feature-extraction
Reward
RL
RFT
Reward Model
custom_code
Instructions to use internlm/POLAR-7B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/POLAR-7B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="internlm/POLAR-7B-Base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("internlm/POLAR-7B-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Add pipeline tag and library name to model card
Verified signatureThis PR improves the model card by adding the `pipeline_tag: text-ranking` to enhance discoverability on the Hugging Face Hub, ensuring users can find your model at https://huggingface.co/models?pipeline_tag=text-ranking. It also adds `library_name: transformers` to correctly associate the model with the Transformers library, enabling the "how to use" code snippets on the model page.2fb9d59
-
Update README_zh-CN.md
Verified signature961acae -
Update README.md
Verified signature30e506d -
Update README.md
Verified signature62ce38e -
Update README_zh-CN.md
Verified signatured488f49 -
Update README.md
Verified signature22287a0
-
upload model
Verified signature7d1554e -
initial commit
Verified signature9254dad