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")
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

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
```

PULSE-7bv5

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{
  "add_prefix_space": false,
  "additional_special_tokens": [
    "<|modelname|>",
    "<|modelorg|>"
  ],
  "bos_token": "<s>",
  "eos_token": "</s>",
  "model_max_length": 1000000000000000019884624838656,
  "name_or_path": "bigscience/bloom",
  "pad_token": "<pad>",
  "padding_side": "left",
  "special_tokens_map_file": null,
  "tokenizer_class": "BloomTokenizerFast",
  "unk_token": "<unk>",
  "chat_template": "Instructions: You are Helper, a large language model full of intelligence. Respond conversationally.</s>{% for message in messages %}{% if message['role'] == 'user' %}User: {{ message['content'] }}</s>{% elif message['role'] == 'assistant' %}Helper: {{ message['content'] }}</s>{% endif %}{% endfor %}{% if add_generation_prompt %}Helper: {% endif %}"
}