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 2 days ago

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1 Parent(s): 8e2a0ef

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@@ -1,4 +1,4 @@
-# 能力评估报告 — Qwen3.8-27B-LabOpen v1
 2026-10-30 · 五侧对比实测，全部为单次评估运行，测试集均为训练未见heldout。
@@ -75,4 +75,4 @@ LabOpen v1 在本探针 greedy 单抽下 0/5 散文，但收获链评估 T0.3 k=
 †带标记口径 ‡无标记口径
-零分归因：运维严格 0 分含约定伪影（空动作 `observe` 为域私有词表、题面未告知；等价重判后 GLM 4/8、DS 3/8、qwen 0/8）与真实条件绑定失败（前沿以 rollback/restart 直觉替代声明规则、无触发题过度动作）；材料严格 0 分为容差伪影（0.01% vs 标准常量，公平口径持平）。诚实表述：运维与遗传为我方独特优势域（条件忠实执行+域词表），材料为语料表精确复现（严格）/物理推理持平（公平），流体物理为本地化能力建设而非独特性。

+# 能力评估报告 — LabOpen v1
 2026-10-30 · 五侧对比实测，全部为单次评估运行，测试集均为训练未见heldout。
 †带标记口径 ‡无标记口径
+零分归因：运维严格 0 分含约定伪影（空动作 `observe` 为域私有词表、题面未告知；等价重判后 GLM 4/8、DS 3/8、qwen 0/8）与真实条件绑定失败（前沿以 rollback/restart 直觉替代声明规则、无触发题过度动作）；材料严格 0 分为容差伪影（0.01% vs 标准常量，公平口径持平）。诚实表述：运维与遗传为我方独特优势域（条件忠实执行+域词表），材料为语料表精确复现（严格）/物理推理持平（公平），流体物理为本地化能力建设而非独特性。