shibing624/medical
Updated • 2.24k • 431
How to use beita6969/deepseek-r1-medical-response with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("beita6969/deepseek-r1-medical-response")
model = AutoModelForCausalLM.from_pretrained("beita6969/deepseek-r1-medical-response")How to use beita6969/deepseek-r1-medical-response with Unsloth Studio:
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for beita6969/deepseek-r1-medical-response to start chatting
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for beita6969/deepseek-r1-medical-response to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for beita6969/deepseek-r1-medical-response to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="beita6969/deepseek-r1-medical-response",
max_seq_length=2048,
)This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
Base model
deepseek-ai/DeepSeek-R1