Question Answering
Transformers
PyTorch
Chinese
baichuan
feature-extraction
lora
custom_code
text-generation-inference
Instructions to use Hongbin37/CBT-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hongbin37/CBT-LLM with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Hongbin37/CBT-LLM", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Hongbin37/CBT-LLM", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from Hongbin37/CBT-LLM: direct link, hf CLI and curl.
- Browser
- Download file 1.14 MB
-
https://huggingface.co/Hongbin37/CBT-LLM/resolve/refs%2Fpr%2F1/tokenizer.model
- Command line
-
hf download hf://Hongbin37/CBT-LLM@refs/pr/1/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/Hongbin37/CBT-LLM/resolve/refs%2Fpr%2F1/tokenizer.model
1.14 MB
- Xet hash:
- cb10bf683b1ce9c5971bf6170d7d95b97eafa413ea531e5796247e32cc2d745f
- Size of remote file:
- 1.14 MB
- SHA256:
- 4be54af290d93c113bcbf421115ae9eed9d6340408f564898f1e966dc738ef01
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