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

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@@ -141,7 +141,8 @@ The model was limited in character count for comparison with the existing T5 mod
 Additionally, further research on prompting engineering is needed due to it producing different results with the same instructions.
 Most metrics for evaluating summarization and generation tasks were somewhat challenging to apply to our study, and while we attempted to address this through the ChatGPT4 Assistant API, future research will be based on feedback from clinicians.
 ## Citation

 Additionally, further research on prompting engineering is needed due to it producing different results with the same instructions.
 Most metrics for evaluating summarization and generation tasks were somewhat challenging to apply to our study, and while we attempted to address this through the ChatGPT4 Assistant API, future research will be based on feedback from clinicians.
+## Non-commercial use
+This models are released exclusively for research purposes and is not intended for commercial use.
 ## Citation