Instructions to use hoangtrung1801/meditron-samsum-clinical-sum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hoangtrung1801/meditron-samsum-clinical-sum with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hoangtrung1801/meditron-samsum-clinical-sum", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use hoangtrung1801/meditron-samsum-clinical-sum 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 hoangtrung1801/meditron-samsum-clinical-sum 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 hoangtrung1801/meditron-samsum-clinical-sum to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for hoangtrung1801/meditron-samsum-clinical-sum to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="hoangtrung1801/meditron-samsum-clinical-sum", max_seq_length=2048, )
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
adapter_model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 319876032
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:789a5bcae2b247f5b7b74b8dff2a80682e441b31fcce7aa9a05b1e727ce01ac4
|
| 3 |
size 319876032
|