Table Question Answering
Transformers
PyTorch
English
llama
text-generation
text-generation-inference
Instructions to use bragour/Camel-7b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bragour/Camel-7b-chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="bragour/Camel-7b-chat")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bragour/Camel-7b-chat") model = AutoModelForCausalLM.from_pretrained("bragour/Camel-7b-chat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from bragour/Camel-7b-chat: direct link, hf CLI and curl.
- Browser
- Download file 500 kB
-
https://huggingface.co/bragour/Camel-7b-chat/resolve/main/tokenizer.model
- Command line
-
hf download hf://bragour/Camel-7b-chat/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/bragour/Camel-7b-chat/resolve/main/tokenizer.model
500 kB
- Xet hash:
- 91bf184ab12793d0754344f9095332759432e666320cc6c07f637af50e36db6f
- Size of remote file:
- 500 kB
- SHA256:
- 9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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