Instructions to use OpenMEDLab/PULSE-7bv5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenMEDLab/PULSE-7bv5 with Transformers:

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

pipe = pipeline("text-generation", model="OpenMEDLab/PULSE-7bv5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenMEDLab/PULSE-7bv5")
model = AutoModelForCausalLM.from_pretrained("OpenMEDLab/PULSE-7bv5", 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 OpenMEDLab/PULSE-7bv5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenMEDLab/PULSE-7bv5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenMEDLab/PULSE-7bv5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenMEDLab/PULSE-7bv5

SGLang

How to use OpenMEDLab/PULSE-7bv5 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 "OpenMEDLab/PULSE-7bv5" \
    --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": "OpenMEDLab/PULSE-7bv5",
		"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 "OpenMEDLab/PULSE-7bv5" \
        --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": "OpenMEDLab/PULSE-7bv5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenMEDLab/PULSE-7bv5 with Docker Model Runner:
```
docker model run hf.co/OpenMEDLab/PULSE-7bv5
```

genggui001 commited on Dec 11, 2023

Commit

631dc98

1 Parent(s): 2739291

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@@ -33,7 +33,7 @@ tags:
 ## 模型介绍
-- **大规模训练**：PULSE模型在OpenChineseLLaMA 13B模型的基础上，
 使用约4,000,000个医学领域和通用领域的SFT数据进行进一步微调。
 - **全面的医学自然语言处理任务**：PULSE支持医学领域的各种自然语
 言处理任务，包括健康教育、医师考试问题、报告解读、医疗记录结构化

 ## 模型介绍
+- **大规模训练**：PULSE模型在Bloom 7B模型的基础上，
 使用约4,000,000个医学领域和通用领域的SFT数据进行进一步微调。
 - **全面的医学自然语言处理任务**：PULSE支持医学领域的各种自然语
 言处理任务，包括健康教育、医师考试问题、报告解读、医疗记录结构化