Reinforcement Learning
Transformers
PyTorch
Safetensors
English
llama
text-generation
text-generation-inference
unsloth
trl
chain-of-thought
cold-start
sft
Instructions to use emredeveloper/DeepSeek-R1-Medical-COT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emredeveloper/DeepSeek-R1-Medical-COT with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("emredeveloper/DeepSeek-R1-Medical-COT") model = AutoModelForCausalLM.from_pretrained("emredeveloper/DeepSeek-R1-Medical-COT") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use emredeveloper/DeepSeek-R1-Medical-COT with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
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 emredeveloper/DeepSeek-R1-Medical-COT to start chatting
Install Unsloth Studio (Windows)
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 emredeveloper/DeepSeek-R1-Medical-COT to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for emredeveloper/DeepSeek-R1-Medical-COT to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="emredeveloper/DeepSeek-R1-Medical-COT", max_seq_length=2048, )
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