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

477f746

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1 Parent(s): 2a57098

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@@ -53,11 +53,13 @@ Use the **merged weights** for serving (vLLM/SGLang). Use the **adapter** on top
 | **Intent understanding** | 10-scene deep-intent probe | **8/10** | preserved |
 | **Formula display** | LaTeX integrity, 5 problems | **5/5** | preserved |
 | Fluid physics (CFM) | Pool-B heldout, T=0.3 k=8, tagged | 44% draws | new (base ≈ 0) |
-| Materials (mixture laws) | heldout 40 items, T=0.3 | 32% draws | new (base ≈ 0) |
 | Classical genetics | heldout 20 items, tagged | 79% draws | new (base: 47% natural) |
-| Ops (action queries) | 8 items, dual-temp | 4–5/8 | new |
 | Prose style | 20 open-ended, T=0.3 k=8 | 75% any-correct | new |
 ### External comparison (same battery, 2026-10)
 | Model | Intent | Agentic tests | Rank |
@@ -156,7 +158,7 @@ The base model can exhaust its thinking budget on unfamiliar domain problems, le
 ## Training methodology (summary)
-15 reinforcement windows (G69–G84) on a frozen FP8 serving stack with exact weight/update contracts: group-relative advantages with k=8 sampling and mechanically verifiable rewards; oracle injection for zero-mastery domains; verified distillation from champion checkpoints; frozen domain tags; substep advantage for multi-step composition; per-window heldout gates and retention protection. Single seed, single run — no capability claim in this card comes from a set seen during training.
 ## Honest boundaries
@@ -233,11 +235,13 @@ Qwen3.8-27B-LabOpen v1 是基于 Qwen3.8-27B 开源基础模型开展领域微
 | **意图理解** | 10 场景深层意图探针 | **8/10** | 保持 |
 | **公式显示** | LaTeX 完整性，5 题 | **5/5** | 保持 |
 | 流体物理（CFM） | heldout 池 B，T=0.3 k=8，带标记 | 44% 采样命中 | 新增（基座 ≈ 0） |
-| 材料（混合律） | heldout 40 项，T=0.3 | 32% 采样命中 | 新增（基座 ≈ 0） |
 | 经典遗传学 | heldout 20 项，带标记 | 79% 采样命中 | 新增（基座自然 47%） |
-| 运维（动作查询） | 8 项，双温 | 4–5/8 | 新增 |
 | 散文文风 | 20 项开放题，T=0.3 k=8 | 75% any-correct | 新增 |
 ### 外部对比（同一测试组，2026-10）
 | 模型 | 意图 | 智能体测试 | 排名 |
@@ -254,7 +258,7 @@ Qwen3.8-27B-LabOpen v1 是基于 Qwen3.8-27B 开源基础模型开展领域微
 上述外部对比模型同时在**各域 heldout 原题**上复测（同一批题、剥离标记的纯题面、k=8 采样 T=0.3、item any-correct、同一套栈评分器、输出统一剥 fence）。
-| 域（题数） | GLM 5.3 | Qwen3.8-max¹ | DeepSeek flash | LabOpen v1² | 基座 |
 |---|---|---|---|---|---|
 | 流体物理（36） | 36/36 | 36/36 | 35/36 | 25/36 † | ≈0 |
 | 材料·严格口径（40） | 1/40 | 0/40 | 0/40 | **17/40** ‡ | ≈0 |
@@ -263,7 +267,7 @@ Qwen3.8-27B-LabOpen v1 是基于 Qwen3.8-27B 开源基础模型开展领域微
 | 运维·严格口径（8） | 0/8 | 0/8 | 0/8 | **4/8**（T0.3） | 0/8 |
 | 运维·空动作等价（8） | 4/8 | 0/8 | 3/8 | 4/8 | — |
-¹ 非思考模式，与智能体对比 battery 口径一致；GLM 5.3 保留完整深思；DeepSeek flash 关闭思考（其 API 选项）。² † 我方带标记部署口径；‡ 我方无标记口径（与外部直接可比）。
 **零分的诚实归因：**
@@ -336,7 +340,7 @@ tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.8-27B-FP8")
 ## 训练方法（摘要）
-15 个强化窗口（G69–G84），运行于带��确权重/更新契约的冻结 FP8 服务栈：组相对优势 + k=8 采样 + 机械可验证奖励；零起点域的 oracle 注入；冠军检查点的验证蒸馏；冻结域标记；多步组合的子步优势；逐窗 heldout 门与保持保护。单种子单次运行——本卡中的全部能力声明均来自训练未见过的评测集。
 ## 诚实边界

 | **Intent understanding** | 10-scene deep-intent probe | **8/10** | preserved |
 | **Formula display** | LaTeX integrity, 5 problems | **5/5** | preserved |
 | Fluid physics (CFM) | Pool-B heldout, T=0.3 k=8, tagged | 44% draws | new (base ≈ 0) |
+| Materials (mixture laws) | heldout 40 items, T=0.3, untagged | 32% draws | new (base ≈ 0) |
 | Classical genetics | heldout 20 items, tagged | 79% draws | new (base: 47% natural) |
+| Ops (action queries) | 8 items, dual-temp | 4/8 (T0.3) – 5/8 (T0.8) | new |
 | Prose style | 20 open-ended, T=0.3 k=8 | 75% any-correct | new |
+The matrix reports draw rates (correct samples / total samples) under the deployment protocol. The same heldout sets, scored at item level (any-correct) and compared zero-shot against frontier models, appear in the Domain-capability comparison section below.
 ### External comparison (same battery, 2026-10)
 | Model | Intent | Agentic tests | Rank |
 ## Training methodology (summary)
+15 reinforcement windows (G69–G84) on a frozen FP8 serving stack with bit-exact checkpoint accounting: group-relative advantages with k=8 sampling and programmatic answer scoring; oracle injection for zero-mastery domains; verified distillation from champion checkpoints; frozen domain tags; substep advantage for multi-step composition; per-window heldout gates and retention protection. Single seed, single run — no capability claim in this card comes from a set seen during training.
 ## Honest boundaries
 | **意图理解** | 10 场景深层意图探针 | **8/10** | 保持 |
 | **公式显示** | LaTeX 完整性，5 题 | **5/5** | 保持 |
 | 流体物理（CFM） | heldout 池 B，T=0.3 k=8，带标记 | 44% 采样命中 | 新增（基座 ≈ 0） |
+| 材料（混合律） | heldout 40 项，T=0.3，无标记 | 32% 采样命中 | 新增（基座 ≈ 0） |
 | 经典遗传学 | heldout 20 项，带标记 | 79% 采样命中 | 新增（基座自然 47%） |
+| 运维（动作查询） | 8 项，双温 | 4/8（T0.3）–5/8（T0.8） | 新增 |
 | 散文文风 | 20 项开放题，T=0.3 k=8 | 75% any-correct | 新增 |
+本表报告部署口径下的采样命中率（正确样本/总样本）。同一批 heldout 题以题目级口径（any-correct）与前沿模型零样本对照，见下方「域独特性实测」一节。
 ### 外部对比（同一测试组，2026-10）
 | 模型 | 意图 | 智能体测试 | 排名 |
 上述外部对比模型同时在**各域 heldout 原题**上复测（同一批题、剥离标记的纯题面、k=8 采样 T=0.3、item any-correct、同一套栈评分器、输出统一剥 fence）。
+| 域（题数） | GLM 5.3 | Qwen3.8-max¹ | DeepSeek flash | LabOpen v1 | 基座 |
 |---|---|---|---|---|---|
 | 流体物理（36） | 36/36 | 36/36 | 35/36 | 25/36 † | ≈0 |
 | 材料·严格口径（40） | 1/40 | 0/40 | 0/40 | **17/40** ‡ | ≈0 |
 | 运维·严格口径（8） | 0/8 | 0/8 | 0/8 | **4/8**（T0.3） | 0/8 |
 | 运维·空动作等价（8） | 4/8 | 0/8 | 3/8 | 4/8 | — |
+¹ 非思考模式，与智能体对比 battery 口径一致；GLM 5.3 保留完整深思；DeepSeek flash 关闭思考（其 API 选项）。† 我方带标记部署口径；‡ 我方无标记口径（与外部直接可比）。
 **零分的诚实归因：**
 ## 训练方法（摘要）
+15 个强化窗口（G69–G84），运行于带逐位精确检查点账目的冻结 FP8 服务栈：组相对优势 + k=8 采样 + 程序化答案评分；零起点域的 oracle 注入；冠军检查点的验证蒸馏；冻结域标记；多步组合的子步优势；逐窗 heldout 门与保持保护。单种子单次运行——本卡中的全部能力声明均来自训练未见过的评测集。
 ## 诚实边界