Instructions to use linjc16/Panacea-7B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use linjc16/Panacea-7B-Chat with Transformers:

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

pipe = pipeline("text-generation", model="linjc16/Panacea-7B-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("linjc16/Panacea-7B-Chat")
model = AutoModelForCausalLM.from_pretrained("linjc16/Panacea-7B-Chat", 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 linjc16/Panacea-7B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "linjc16/Panacea-7B-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "linjc16/Panacea-7B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/linjc16/Panacea-7B-Chat

SGLang

How to use linjc16/Panacea-7B-Chat 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 "linjc16/Panacea-7B-Chat" \
    --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": "linjc16/Panacea-7B-Chat",
		"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 "linjc16/Panacea-7B-Chat" \
        --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": "linjc16/Panacea-7B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use linjc16/Panacea-7B-Chat with Docker Model Runner:
```
docker model run hf.co/linjc16/Panacea-7B-Chat
```

Panacea-7B-Chat / README.md

linjc16

Update README.md

3f7875c verified over 1 year ago

preview code

Raw

History Blame Contribute Delete

1.89 kB

	---
	license: apache-2.0
	language:
	- en
	base_model:
	- mistralai/Mistral-7B-v0.1
	pipeline_tag: text-generation
	tags:
	- clinical trial
	- foundation model
	---

	# Model Card for Panacea-7B-Chat
	The Panacea-7B-Chat is a foundation model for clinical trial search, summarization, design, and recruitment. It was equipped with clinical knowledge by being trained on 793,279 clinical trial design documents
	worldwide and 1,113,207 clinical study papers. It shows superior performances than various open-sourced LLMs and medical LLMs on clinical trial tasks.

	For full details of this model please read our [paper](https://arxiv.org/abs/2407.11007).

	## Model Training
	Panacea is trained from [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). The training of Panacea consists of an alignment step and an instruction-tuning step.
	* Alignment step: continued pre-training on a large collection of trial documents and trial-related scientific papers. This step adapts Panacea to the vocabulary commonly used in clinical trials.
	* Instruction-tuning step: further enables Panacea to comprehend the user explanation of the task definition and the output requirement.

	Load the model in the following way (same as Mistral):

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model_id = 'linjc16/Panacea-7B-Chat'

	model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	```

	## Citation
	If you find our paper or models helpful, please consider cite as follows:

	```bibtex
	@article{lin2024panacea,
	title={Panacea: A foundation model for clinical trial search, summarization, design, and recruitment},
	author={Lin, Jiacheng and Xu, Hanwen and Wang, Zifeng and Wang, Sheng and Sun, Jimeng},
	journal={arXiv preprint arXiv:2407.11007},
	year={2024}
	}
	```