Instructions to use HoomanBio2021/LabOpen-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HoomanBio2021/LabOpen-v1 with Transformers:

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

pipe = pipeline("text-generation", model="HoomanBio2021/LabOpen-v1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("HoomanBio2021/LabOpen-v1")
model = AutoModelForMultimodalLM.from_pretrained("HoomanBio2021/LabOpen-v1", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HoomanBio2021/LabOpen-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HoomanBio2021/LabOpen-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HoomanBio2021/LabOpen-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HoomanBio2021/LabOpen-v1

SGLang

How to use HoomanBio2021/LabOpen-v1 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 "HoomanBio2021/LabOpen-v1" \
    --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": "HoomanBio2021/LabOpen-v1",
		"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 "HoomanBio2021/LabOpen-v1" \
        --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": "HoomanBio2021/LabOpen-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HoomanBio2021/LabOpen-v1 with Docker Model Runner:
```
docker model run hf.co/HoomanBio2021/LabOpen-v1
```

HoomanBio commited on 5 days ago

Commit

151f498

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1 Parent(s): 903f05c

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@@ -14,7 +14,7 @@ FP8 能力 GPU（H100 / GB10 / B200 级别），FP8 下显存 ~28GB
 ### 2. 方式一：合并权重直接服务（推荐）
 ```bash
-vllm serve HoomanBio/Qwen3.8-27B-LabOpen-v1
 ```
 合并权重与基座 `Qwen/Qwen3.8-27B-FP8` 采用完全相同的分块 FP8（e4m3，128×128 块尺度）张量布局与文件结构，任何能加载基座的运行时（vLLM / SGLang）均可直接加载本仓库，无需任何改动。
@@ -27,7 +27,7 @@ from peft import PeftModel
 base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.8-27B-FP8", device_map="auto")
 model = PeftModel.from_pretrained(
-    base, "HoomanBio/Qwen3.8-27B-LabOpen-v1", subfolder="adapter")
 tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.8-27B-FP8")
 model.eval()
 ```

 ### 2. 方式一：合并权重直接服务（推荐）
 ```bash
+vllm serve HoomanBio2021/Qwen3.8-27B-LabOpen-v1
 ```
 合并权重与基座 `Qwen/Qwen3.8-27B-FP8` 采用完全相同的分块 FP8（e4m3，128×128 块尺度）张量布局与文件结构，任何能加载基座的运行时（vLLM / SGLang）均可直接加载本仓库，无需任何改动。
 base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.8-27B-FP8", device_map="auto")
 model = PeftModel.from_pretrained(
+    base, "HoomanBio2021/Qwen3.8-27B-LabOpen-v1", subfolder="adapter")
 tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.8-27B-FP8")
 model.eval()
 ```