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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("internlm/POLAR-7B-Base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -72,6 +72,12 @@ You could employ the latest [xtuner](https://github.com/InternLM/xtuner) to fine
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- Install xtuner via pip
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```shell
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pip install 'git+https://github.com/InternLM/xtuner.git@main#egg=xtuner[deepspeed]'
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```
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- Install xtuner via pip
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```shell
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pip install 'xtuner[deepspeed]'==0.2.0
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```
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- Install xtuner from the latest source code
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```shell
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pip install 'git+https://github.com/InternLM/xtuner.git@main#egg=xtuner[deepspeed]'
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```
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