Instructions to use jinee/note with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jinee/note with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="jinee/note")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jinee/note")
model = AutoModelForCausalLM.from_pretrained("jinee/note", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jinee/note with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jinee/note"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jinee/note",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jinee/note

SGLang

How to use jinee/note with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "jinee/note" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jinee/note",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "jinee/note" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jinee/note",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jinee/note with Docker Model Runner:
```
docker model run hf.co/jinee/note
```

jinee commited on Feb 19, 2024

Commit

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+---
+license: cc-by-nc-sa-4.0
+language:
+- en
+tags:
+- medical
+---
+# NOTE
+Notable generation Of patient Text summaries through an Efficient approach based on direct preference optimization (DPO)
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+## Model Details
+### Model Description
+- **Model type:** MistralForCausalLM
+- **Language(s) (NLP):** English
+- **License:** [CC-BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+- **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+### Model Sources
+- **Paper:** [NOTE](arvix.)
+- **Demo:** [NOTE-DEMO](https://huggingface.co/spaces/jinee/note-demo)
+## Usage
+## Dataset
+The model has been trained on a [MIMIC-III](https://physionet.org/content/mimiciii/1.4/), a comprehensive and freely accssible de-identified medical database.
+Access to this databased requires a number of steps to obtain permission.
+## Clinical appli
+## Limitations
+## Citation
+**BibTeX:**
+[More Information Needed]
+**APA:**
+[More Information Needed]
+## INMED DATA
+[More Information Needed]