Instructions to use prithvi1029/deepseek-medquad-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithvi1029/deepseek-medquad-lora 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="prithvi1029/deepseek-medquad-lora")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithvi1029/deepseek-medquad-lora", device_map="auto") - PEFT
How to use prithvi1029/deepseek-medquad-lora with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from prithvi1029/deepseek-medquad-lora: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/prithvi1029/deepseek-medquad-lora/resolve/main/tokenizer.json
- Command line
-
hf download hf://prithvi1029/deepseek-medquad-lora/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/prithvi1029/deepseek-medquad-lora/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 3765bb10affc44c013b0047c2833cda17fd69a3408006dcfe1e4f53d04d1286e
- Size of remote file:
- 11.4 MB
- SHA256:
- 7b5aef614e5d3425ddd97623eabba0f904b5fa77c8db08d3624e4721dae7323a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.