Instructions to use sdinger/bloomz-560m_PROMPT_TUNING_CAUSAL_LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sdinger/bloomz-560m_PROMPT_TUNING_CAUSAL_LM with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m") model = PeftModel.from_pretrained(base_model, "sdinger/bloomz-560m_PROMPT_TUNING_CAUSAL_LM") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from sdinger/bloomz-560m_PROMPT_TUNING_CAUSAL_LM: direct link, hf CLI and curl.
- Browser
- Download file 34 kB
-
https://huggingface.co/sdinger/bloomz-560m_PROMPT_TUNING_CAUSAL_LM/resolve/main/adapter_model.bin
- Command line
-
hf download hf://sdinger/bloomz-560m_PROMPT_TUNING_CAUSAL_LM/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/sdinger/bloomz-560m_PROMPT_TUNING_CAUSAL_LM/resolve/main/adapter_model.bin
34 kB
- Xet hash:
- d99a64d71e121cedc5de885cd245f58e35244358317e523e201f5d495f76b339
- Size of remote file:
- 34 kB
- SHA256:
- 1cf00e548a89d77aee2ea07b93d04bdc911978c0b0c4be18e5c872570c021d6c
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