Instructions to use OpenMEDLab/PULSE-20bv5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenMEDLab/PULSE-20bv5 with Transformers:

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

pipe = pipeline("text-generation", model="OpenMEDLab/PULSE-20bv5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenMEDLab/PULSE-20bv5")
model = AutoModelForCausalLM.from_pretrained("OpenMEDLab/PULSE-20bv5", 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-20bv5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenMEDLab/PULSE-20bv5"
# 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-20bv5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenMEDLab/PULSE-20bv5

SGLang

How to use OpenMEDLab/PULSE-20bv5 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-20bv5" \
    --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-20bv5",
		"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-20bv5" \
        --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-20bv5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenMEDLab/PULSE-20bv5 with Docker Model Runner:
```
docker model run hf.co/OpenMEDLab/PULSE-20bv5
```

genggui001 commited on Dec 14, 2023

Commit

568962b

1 Parent(s): 475cc92

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@@ -43,6 +43,24 @@ tags:
 由于模型参数量较小和自回归生成范式，尽管模型提供了有关疾病诊断和治疗的推理结果，但这些结果不能代替线下职业医生的建议和治疗方案。所有回答仅供参考，不应作为诊断或治疗的依据。我们强烈建议用户在需要诊断或治疗疾病时，寻求专业医生的帮助和建议。
 ## 推理

 由于模型参数量较小和自回归生成范式，尽管模型提供了有关疾病诊断和治疗的推理结果，但这些结果不能代替线下职业医生的建议和治疗方案。所有回答仅供参考，不应作为诊断或治疗的依据。我们强烈建议用户在需要诊断或治疗疾病时，寻求专业医生的帮助和建议。
+### Elo评测
+| Model Name   |   AVG Rank |   MedQA-USMLE |   MedQA-Mainland |   PromptCBLUE |   WebMedQA |   CheckupQA |   MedicineQA |   DialogSumm |   MedTriage (F1) |
+|:-------------|-----------:|--------------:|-----------------:|--------------:|-----------:|------------:|-------------:|-------------:|-----------------:|
+| GPT-4        |       1.25 |          1129 |             1117 |          1110 |       1116 |        1096 |         1098 |         1109 |             0.65 |
+| PULSE-Pro    |       1.75 |          1089 |             1092 |          1088 |       1119 |        1105 |         1083 |         1096 |             0.63 |
+| ChatGPT      |       4.00 |          1086 |             1057 |          1064 |       1053 |        1020 |         1029 |         1080 |             0.43 |
+| PULSE-20b     |       4.12 |          1042 |             1024 |          1039 |       1059 |        1049 |         1069 |         1076 |             0.40 |
+| Baichuan2    |       4.50 |          1024 |             1041 |          1065 |       1044 |        1062 |         1035 |         1069 |             0.33 |
+| ChatGLM3     |       5.62 |          1038 |             1062 |           997 |       1012 |        1003 |         1024 |         1021 |             0.06 |
+| HuatuoGPT2   |       7.62 |           955 |              993 |           985 |        963 |         983 |         1003 |          980 |             0.01 |
+| QiZhenGPT    |       8.38 |           955 |              959 |           945 |        989 |        1039 |          932 |          921 |             0.00 |
+| BenTsao      |       8.75 |           961 |              921 |           936 |        910 |         927 |          986 |          920 |             0.02 |
+| BianQue2     |      10.12 |           913 |              928 |           919 |        988 |         974 |          900 |          908 |             0.00 |
+| MING         |      10.75 |           902 |              909 |           924 |        867 |         862 |          960 |          918 |             0.01 |
+| DoctorGLM    |      11.12 |           906 |              896 |           930 |        879 |         880 |          880 |          905 |             0.00 |
+** PULSE-20b=PULSE-20bv5 **
 ## 推理