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

ae49e20

verified ·

1 Parent(s): 45ffe75

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@@ -27,7 +27,7 @@ LabOpen v1 adds six verifiable capability domains (fluid physics, materials mixt
 - **Base model**: Qwen3.8-27B (FP8 release, Apache 2.0)
 - **Training**: 15 reinforcement windows (G69–G84), LoRA r=4, group-relative RL (RLOO-style, k=8 sampling) with mechanically verifiable rewards
 - **License**: Apache 2.0 (same as base)
-- **Hardware target**: any FP8-capable GPU (H100 / GB10 / B200 class); ~28 GB in FP8
 ## Positioning & Philosophy
@@ -37,7 +37,6 @@ The project does not aim to replace general-purpose large language models. It se
 The model is intended for universities, research institutes, corporate R&D centers, and professional laboratories, and supports local or private deployment scaled to the institution's actual computing resources.
-We believe that AI for scientific research should not depend solely on remote general-purpose model services. Research institutions deserve the opportunity to deploy, operate, and continuously refine specialized models on their own computing infrastructure — forming AI capability that fits their own research missions, data-governance requirements, and domain expertise.
 ## What's in this repo
@@ -174,8 +173,8 @@ The base model can exhaust its thinking budget on unfamiliar domain problems, le
 ## Acknowledgments
 - **Alibaba Qwen team** — for Qwen3.8-27B, the open base model this work builds on, and for the open Apache-2.0 release that makes derivative research like this possible.
-- **Z.ai (Zhipu AI)** — GLM 5.3 served as the strongest external benchmark in our evaluation battery and as an engineering assistant during the release workflow.
-- **Moonshot AI** — Kimi K3's public work on long-context and agentic reasoning informed our evaluation design for intent understanding and multi-step tool use.
 We thank these teams for pushing open, measurable frontiers — this release aims to contribute back in the same spirit.
@@ -209,7 +208,7 @@ LabOpen v1 在 [Qwen/Qwen3.8-27B-FP8](https://huggingface.co/Qwen/Qwen3.8-27B-FP
 - **基座模型**：Qwen3.8-27B（FP8 版本，Apache 2.0）
 - **训练**：15 个强化窗口（G69–G84），LoRA r=4，组相对强化学习（RLOO 式，k=8 采样），机械可验证奖励
 - **许可证**：Apache 2.0（与基座相同）
-- **硬件要求**：任意支持 FP8 的 GPU（H100 / GB10 / B200 级别）；FP8 下约 28 GB
 ## 项目定位与理念
@@ -219,7 +218,6 @@ Qwen3.8-27B-LabOpen v1 是基于 Qwen3.8-27B 开源基础模型开展领域微
 模型主要面向高校、科研院所、企业研发中心及专业实验室等使用场景，支持根据实际计算资源条件进行本地化或私有化部署。
-我们相信，科学研究中的人工智能不应仅仅依赖远程通用模型服务。科研机构也应有机会在自身的计算基础设施上部署、使用和持续优化专业模型，形成符合本机构研究任务、数据管理要求和专业知识体系的人工智能能力。
 ## 仓库内容
@@ -355,8 +353,8 @@ vllm serve HoomanBio/Qwen3.8-27B-LabOpen-v1
 ## 致谢
 - **阿里巴巴 Qwen 团队**——感谢 Qwen3.8-27B 这一开放基座模型，以及 Apache-2.0 开放发布使此类衍生研究成为可能。
-- **智谱 AI（Z.ai）**——GLM 5.3 在我们的评估测试组中是最强外部基准，并在发布工程流程中担任工程助手。
-- **月之暗面（Moonshot AI）**——Kimi K3 在长上下文与智能体推理上的公开工作，为我们的意图理解与多步工具使用评测设计提供了参考。
 我们感谢这些团队推动开放、可度量的前沿——本次发布希望以同样的精神回馈社区。

 - **Base model**: Qwen3.8-27B (FP8 release, Apache 2.0)
 - **Training**: 15 reinforcement windows (G69–G84), LoRA r=4, group-relative RL (RLOO-style, k=8 sampling) with mechanically verifiable rewards
 - **License**: Apache 2.0 (same as base)
+- **Hardware target**: FP8-capable GPUs (e.g., NVIDIA H100 / DGX GB10 class); ~31 GB in FP8
 ## Positioning & Philosophy
 The model is intended for universities, research institutes, corporate R&D centers, and professional laboratories, and supports local or private deployment scaled to the institution's actual computing resources.
 ## What's in this repo
 ## Acknowledgments
 - **Alibaba Qwen team** — for Qwen3.8-27B, the open base model this work builds on, and for the open Apache-2.0 release that makes derivative research like this possible.
+- **Z.ai (Zhipu AI)** — GLM 5.3 served as one of the external benchmarks in our evaluation battery and as an engineering assistant during the research and release workflow.
+- **Moonshot AI** — Kimi K3 provided engineering assistance in experiment design and testing during the early research process.
 We thank these teams for pushing open, measurable frontiers — this release aims to contribute back in the same spirit.
 - **基座模型**：Qwen3.8-27B（FP8 版本，Apache 2.0）
 - **训练**：15 个强化窗口（G69–G84），LoRA r=4，组相对强化学习（RLOO 式，k=8 采样），机械可验证奖励
 - **许可证**：Apache 2.0（与基座相同）
+- **硬件要求**：支持 FP8 的 GPU（如 NVIDIA H100 / DGX GB10 等级别）；FP8 下约 31 GB
 ## 项目定位与理念
 模型主要面向高校、科研院所、企业研发中心及专业实验室等使用场景，支持根据实际计算资源条件进行本地化或私有化部署。
 ## 仓库内容
 ## 致谢
 - **阿里巴巴 Qwen 团队**——感谢 Qwen3.8-27B 这一开放基座模型，以及 Apache-2.0 开放发布使此类衍生研究成为可能。
+- **智谱 AI（Z.ai）**——GLM 5.3 在我们的评估测试组中作为外部基准之一，并在研究与发布流程中担任工程助手。
+- **月之暗面（Moonshot AI）**——Kimi K3 在前期研究进程中的实验设计和测试中提供了工程协助。
 我们感谢这些团队推动开放、可度量的前沿——本次发布希望以同样的精神回馈社区。