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

903f05c

verified ·

1 Parent(s): ae49e20

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README.md CHANGED Viewed

@@ -104,8 +104,8 @@ The five external-comparison models above were also run on the **domain heldout
 ### Serving the merged weights (recommended)
 ```bash
-vllm serve HoomanBio/Qwen3.8-27B-LabOpen-v1
-# or: docker run ... vllm/vllm-openai --model HoomanBio/Qwen3.8-27B-LabOpen-v1
 ```
 The merged checkpoint uses the same blockwise FP8 (e4m3, 128×128 scales) format as the base — any runtime that loads `Qwen/Qwen3.8-27B-FP8` loads this without changes.
@@ -185,7 +185,7 @@ We thank these teams for pushing open, measurable frontiers — this release aim
   title = {Qwen3.8-27B-LabOpen v1: Six-Domain Reinforcement of Qwen3.8-27B},
   author = {Hooman AI Lab},
   year = {2026},
-  url = {https://huggingface.co/HoomanBio/Qwen3.8-27B-LabOpen-v1},
   base = {Qwen/Qwen3.8-27B-FP8},
   license = {Apache-2.0}
 }
@@ -284,8 +284,8 @@ Qwen3.8-27B-LabOpen v1 是基于 Qwen3.8-27B 开源基础模型开展领域微
 ### 部署合并权重（推荐）
 ```bash
-vllm serve HoomanBio/Qwen3.8-27B-LabOpen-v1
-# 或：docker run ... vllm/vllm-openai --model HoomanBio/Qwen3.8-27B-LabOpen-v1
 ```
 合并检查点与基座采用完全相同的分块 FP8（e4m3，128×128 块尺度）格式——任何能加载 `Qwen/Qwen3.8-27B-FP8` 的运行时均可直接加载本仓库，无需改动。
@@ -365,7 +365,7 @@ vllm serve HoomanBio/Qwen3.8-27B-LabOpen-v1
   title = {Qwen3.8-27B-LabOpen v1: Six-Domain Reinforcement of Qwen3.8-27B},
   author = {Hooman AI Lab},
   year = {2026},
-  url = {https://huggingface.co/HoomanBio/Qwen3.8-27B-LabOpen-v1},
   base = {Qwen/Qwen3.8-27B-FP8},
   license = {Apache-2.0}
 }

 ### Serving the merged weights (recommended)
 ```bash
+vllm serve HoomanBio2021/Qwen3.8-27B-LabOpen-v1
+# or: docker run ... vllm/vllm-openai --model HoomanBio2021/Qwen3.8-27B-LabOpen-v1
 ```
 The merged checkpoint uses the same blockwise FP8 (e4m3, 128×128 scales) format as the base — any runtime that loads `Qwen/Qwen3.8-27B-FP8` loads this without changes.
   title = {Qwen3.8-27B-LabOpen v1: Six-Domain Reinforcement of Qwen3.8-27B},
   author = {Hooman AI Lab},
   year = {2026},
+  url = {https://huggingface.co/HoomanBio2021/Qwen3.8-27B-LabOpen-v1},
   base = {Qwen/Qwen3.8-27B-FP8},
   license = {Apache-2.0}
 }
 ### 部署合并权重（推荐）
 ```bash
+vllm serve HoomanBio2021/Qwen3.8-27B-LabOpen-v1
+# 或：docker run ... vllm/vllm-openai --model HoomanBio2021/Qwen3.8-27B-LabOpen-v1
 ```
 合并检查点与基座采用完全相同的分块 FP8（e4m3，128×128 块尺度）格式——任何能加载 `Qwen/Qwen3.8-27B-FP8` 的运行时均可直接加载本仓库，无需改动。
   title = {Qwen3.8-27B-LabOpen v1: Six-Domain Reinforcement of Qwen3.8-27B},
   author = {Hooman AI Lab},
   year = {2026},
+  url = {https://huggingface.co/HoomanBio2021/Qwen3.8-27B-LabOpen-v1},
   base = {Qwen/Qwen3.8-27B-FP8},
   license = {Apache-2.0}
 }