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

a2c362f

verified ·

1 Parent(s): 477f746

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README.md +15 -18

README.md CHANGED Viewed

@@ -8,7 +8,14 @@ tags:
 - fp8
 - agentic-coding
 - scientific-computing
 library_name: transformers
 ---
 # Qwen3.8-27B-LabOpen v1
@@ -38,7 +45,7 @@ We believe that AI for scientific research should not depend solely on remote ge
 | Path | Content |
 |---|---|
 | `model-0000X-of-00007.safetensors` + index | **Merged FP8 full weights** (base + LoRA, blockwise-FP8 re-quantized). Same tensor layout and quantization format as the base — drop-in replacement |
-| `adapter/` | **LoRA adapter** (112 MB, PEFT format, 992 tensors). Numerically exact training artifact — no re-quantization |
 | `MERGE_REPORT.md` | Merge provenance: digests, shard SHA-256, re-quantization error stats, output-equivalence probes |
 | `EVALUATION.md` | Measured capability matrix and 5-model external comparison |
 | `USAGE.md` | Loading, domain tags, temperature guidance, troubleshooting |
@@ -104,16 +111,11 @@ vllm serve 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.
-### Loading the adapter (exact training numerics)
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-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")
-```
 > **Do not run `merge_and_unload()` on the FP8 base yourself.** The base stores weights as blockwise-FP8 codes with 128×128 scales; a naive PEFT merge adds a BF16 delta directly onto FP8 codes and silently corrupts the weights. Use the merged checkpoint provided here — it was produced by dequantize → add delta → re-quantize with per-block scale recomputation and verified against the adapter (see `MERGE_REPORT.md`).
@@ -220,7 +222,7 @@ Qwen3.8-27B-LabOpen v1 是基于 Qwen3.8-27B 开源基础模型开展领域微
 | 路径 | 内容 |
 |---|---|
 | `model-0000X-of-00007.safetensors` + index | **合并 FP8 全权重**（基座 + LoRA，分块 FP8 重量化）。张量布局与量化格式和基座完全相同——即插即用替换 |
-| `adapter/` | **LoRA 适配器**（112 MB，PEFT 格式，992 张量）。训练时的精确数值工件，无重量化 |
 | `MERGE_REPORT.md` | 合并溯源：摘要、分片 SHA-256、重量化误差统计、输出等价探针 |
 | `EVALUATION.md` | 实测能力矩阵与五模型外部对比 |
 | `USAGE.md` | 加载方式、域标记、温度建议、故障排查 |
@@ -286,16 +288,11 @@ vllm serve 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。
-### 加载适配器（训练时精确数值）
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-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")
-```
 > **不要自行对 FP8 基座执行 `merge_and_unload()`。** 基座权重以分块 FP8 码 + 128×128 块尺度存储；朴素 PEFT 合并会把 BF16 增量直接加到 FP8 码上，静默损坏权重。请使用本仓库提供的合并检查点——它按「反量化 → 加增量 → 按块重量化」生成，并经与适配器的等价探针验证（见 `MERGE_REPORT.md`）。

 - fp8
 - agentic-coding
 - scientific-computing
+- physics
+- materials-science
+- genetics
+language:
+- en
+- zh
 library_name: transformers
+pipeline_tag: text-generation
 ---
 # Qwen3.8-27B-LabOpen v1
 | Path | Content |
 |---|---|
 | `model-0000X-of-00007.safetensors` + index | **Merged FP8 full weights** (base + LoRA, blockwise-FP8 re-quantized). Same tensor layout and quantization format as the base — drop-in replacement |
+| `adapter/` | **LoRA adapter** (112 MB, PEFT format, 992 tensors). Exact training artifact for inspection and further training — serve the merged weights instead |
 | `MERGE_REPORT.md` | Merge provenance: digests, shard SHA-256, re-quantization error stats, output-equivalence probes |
 | `EVALUATION.md` | Measured capability matrix and 5-model external comparison |
 | `USAGE.md` | Loading, domain tags, temperature guidance, troubleshooting |
 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.
+**Deployment notes (tested on vLLM 0.27.1):** give domain-computation requests `max_tokens ≥ 4096` — the model reasons before answering, and a 1024 budget is exhausted by the reasoning chain (`finish_reason=length`, empty content). With vLLM's `--reasoning-parser qwen3` the final answer arrives in `content` and reasoning in `reasoning_content`.
+### The adapter (research artifact — not a serving path)
+The `adapter/` directory holds the exact training artifact (PEFT format, 112 MB) for inspection, provenance and further training. It is **not** a drop-in serving path: plain transformers cannot load the blockwise-FP8 base, and vLLM's LoRA kernels do not cover this architecture's hybrid linear-attention projections (`in_proj_*`, `out_proj`) — 5 of 12 target module types would be silently skipped, corrupting behavior. **Serve the merged weights.**
 > **Do not run `merge_and_unload()` on the FP8 base yourself.** The base stores weights as blockwise-FP8 codes with 128×128 scales; a naive PEFT merge adds a BF16 delta directly onto FP8 codes and silently corrupts the weights. Use the merged checkpoint provided here — it was produced by dequantize → add delta → re-quantize with per-block scale recomputation and verified against the adapter (see `MERGE_REPORT.md`).
 | 路径 | 内容 |
 |---|---|
 | `model-0000X-of-00007.safetensors` + index | **合并 FP8 全权重**（基座 + LoRA，分块 FP8 重量化）。张量布局与量化格式和基座完全相同——即插即用替换 |
+| `adapter/` | **LoRA 适配器**（112 MB，PEFT 格式，992 张量）。训练精确工件，供检查与继续训练——服务请用合并权重 |
 | `MERGE_REPORT.md` | 合并溯源：摘要、分片 SHA-256、重量化误差统计、输出等价探针 |
 | `EVALUATION.md` | 实测能力矩阵与五模型外部对比 |
 | `USAGE.md` | 加载方式、域标记、温度建议、故障排查 |
 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。
+**部署要点（已在 vLLM 0.27.1 实测）**：域计算请求的 `max_tokens` 应给到 **4096 以上**——模型先思考后作答，1024 预算会被思考链耗尽（`finish_reason=length`、正文为空）。vLLM 配 `--reasoning-parser qwen3` 时，最终答案在 `content`、推理在 `reasoning_content`。
+### 适配器（研究工件——非服务路径）
+`adapter/` 目录保存训练时的精确工件（PEFT 格式，112 MB），供检查、溯源与继续训练使用。它**���是**即插即用的服务路径：原生 transformers 无法加载分块 FP8 基座；vLLM 的 LoRA 内核不覆盖本架构混合线性注意力投影（`in_proj_*`、`out_proj`）——12 类目标模块中 5 类会被静默跳过、损坏行为。**服务请使用合并权重。**
 > **不要自行对 FP8 基座执行 `merge_and_unload()`。** 基座权重以分块 FP8 码 + 128×128 块尺度存储；朴素 PEFT 合并会把 BF16 增量直接加到 FP8 码上，静默损坏权重。请使用本仓库提供的合并检查点——它按「反量化 → 加增量 → 按块重量化」生成，并经与适配器的等价探针验证（见 `MERGE_REPORT.md`）。