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
```

LabOpen-v1 / MERGE_REPORT.md

HoomanBio

Update MERGE_REPORT.md

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# Merge Report — LabOpen v1 merged FP8 weights

This document records how the merged full-weight checkpoint in this repository was produced from the LoRA adapter, and how it was verified. Internal filesystem paths are omitted; digests are reproduced verbatim from the training run.

## Method

Per target module (LoRA r=4, alpha=4, scaling 1.0, 496 modules = 400 blockwise-FP8 + 96 BF16):

1. Dequantize base weight: `W = codes * scale` per 128x128 block (FP32)
2. Add LoRA delta: `W_merged = W + (B @ A) * scaling`
3. Re-quantize per 128x128 block: `scale = amax(|block|)/448`, codes clamped to +-448 (e4m3)

Non-target tensors are byte-copies of the base. Tensor layout, shard split and file format are identical to the base FP8 release.

## Provenance

- Trainable digest (checkpoint B): `96ef57e87e0d64de0340f9b917f4b9a7276356908bc6a5a7e59caa594bf68e03`
- FP8 modules merged: 400; BF16 modules merged: 96; override tensors written: 896 (400 weights + 400 scales + 96 weights)
- Total checkpoint size: 30,866,663,264 bytes

## Re-quantization error (vs exact FP32 merged weights)

- Mean relative error: 0.330%
- Max relative error: 1.00 (isolated near-zero elements; same order as the FP8 storage noise the base itself carries)

## Output-equivalence probes (greedy, 3 prompts x 48 tokens)

A. In-memory merged model vs adapter (scales in FP32):

| Prompt | Prefix-equal tokens | Token match |
|---|---|---|
| A 2 kg block slides down a frictionless ... | 41/48 | 87.5% |
| def quicksort(arr):
    if len(arr) <= 1... | 48/48 | 100.0% |
| Water flows at 3 m/s through a pipe of d... | 1/48 | 4.2% |
B. **Saved-and-reloaded checkpoint (the published artifact, scales serialized BF16) vs adapter — release gate:**

| Prompt | Prefix-equal tokens | Token match |
|---|---|---|
| prompt 0 | 48/48 | 100.0% |
| prompt 1 | 48/48 | 100.0% |
| prompt 2 | 48/48 | 100.0% |

**Gate result: PASSED — 3/3 prompts, 144/144 tokens identical to the adapter trajectory.**

## Shard SHA-256

| File | SHA-256 | Bytes |
|---|---|---|
| `model-00001-of-00007.safetensors` | `3eeeab65368b86c1eec3582495f5367c9a6a6ac25f0857adbe7f8026ea322d3f` | 3,969,317,448 |
| `model-00002-of-00007.safetensors` | `7091dbd7077b8d19bb547eaf24997ec3c2b6b0acef8f2678f1a7a6a15c212a3e` | 4,939,321,704 |
| `model-00003-of-00007.safetensors` | `7ec75e6485b732125098c0080669ddbcef007be82bd6830a07e566b9a9ee3264` | 4,939,863,424 |
| `model-00004-of-00007.safetensors` | `44fef978664d99a87d6c4e5826e51486eb4e28788ca791b694b29430432a12fe` | 4,971,328,696 |
| `model-00005-of-00007.safetensors` | `f815eef9e6ed5194fdc0ac7412ad29019661ca6062bcd454fb4ce341834e29a4` | 4,955,596,064 |
| `model-00006-of-00007.safetensors` | `d2de7016e251a68f0a7ba2c49615c243c20ad87dfaacca252d6b2c0277f5fbbb` | 4,071,440,632 |
| `model-00007-of-00007.safetensors` | `1bed352a0625d22dc606de47fd3563f24fd9fa73915c4ddffe7947c4b56c28bf` | 3,019,999,168 |

## Notes

- The adapter (`adapter/`) remains the numerically exact training artifact; the merged checkpoint adds only the measured re-quantization error above.
- Probes ran on the training stack runtime (identical kernels to the serving configuration used for evaluation).

	# Merge Report — LabOpen v1 merged FP8 weights

	This document records how the merged full-weight checkpoint in this repository was produced from the LoRA adapter, and how it was verified. Internal filesystem paths are omitted; digests are reproduced verbatim from the training run.

	## Method

	Per target module (LoRA r=4, alpha=4, scaling 1.0, 496 modules = 400 blockwise-FP8 + 96 BF16):

	1. Dequantize base weight: `W = codes * scale` per 128x128 block (FP32)
	2. Add LoRA delta: `W_merged = W + (B @ A) * scaling`
	3. Re-quantize per 128x128 block: `scale = amax(\|block\|)/448`, codes clamped to +-448 (e4m3)

	Non-target tensors are byte-copies of the base. Tensor layout, shard split and file format are identical to the base FP8 release.

	## Provenance

	- Trainable digest (checkpoint B): `96ef57e87e0d64de0340f9b917f4b9a7276356908bc6a5a7e59caa594bf68e03`
	- FP8 modules merged: 400; BF16 modules merged: 96; override tensors written: 896 (400 weights + 400 scales + 96 weights)
	- Total checkpoint size: 30,866,663,264 bytes

	## Re-quantization error (vs exact FP32 merged weights)

	- Mean relative error: 0.330%
	- Max relative error: 1.00 (isolated near-zero elements; same order as the FP8 storage noise the base itself carries)

	## Output-equivalence probes (greedy, 3 prompts x 48 tokens)

	A. In-memory merged model vs adapter (scales in FP32):

	\| Prompt \| Prefix-equal tokens \| Token match \|
	\|---\|---\|---\|
	\| A 2 kg block slides down a frictionless ... \| 41/48 \| 87.5% \|
	\| def quicksort(arr):
	if len(arr) <= 1... \| 48/48 \| 100.0% \|
	\| Water flows at 3 m/s through a pipe of d... \| 1/48 \| 4.2% \|

	B. Saved-and-reloaded checkpoint (the published artifact, scales serialized BF16) vs adapter — release gate:

	\| Prompt \| Prefix-equal tokens \| Token match \|
	\|---\|---\|---\|
	\| prompt 0 \| 48/48 \| 100.0% \|
	\| prompt 1 \| 48/48 \| 100.0% \|
	\| prompt 2 \| 48/48 \| 100.0% \|

	Gate result: PASSED — 3/3 prompts, 144/144 tokens identical to the adapter trajectory.

	## Shard SHA-256

	\| File \| SHA-256 \| Bytes \|
	\|---\|---\|---\|
	\| `model-00001-of-00007.safetensors` \| `3eeeab65368b86c1eec3582495f5367c9a6a6ac25f0857adbe7f8026ea322d3f` \| 3,969,317,448 \|
	\| `model-00002-of-00007.safetensors` \| `7091dbd7077b8d19bb547eaf24997ec3c2b6b0acef8f2678f1a7a6a15c212a3e` \| 4,939,321,704 \|
	\| `model-00003-of-00007.safetensors` \| `7ec75e6485b732125098c0080669ddbcef007be82bd6830a07e566b9a9ee3264` \| 4,939,863,424 \|
	\| `model-00004-of-00007.safetensors` \| `44fef978664d99a87d6c4e5826e51486eb4e28788ca791b694b29430432a12fe` \| 4,971,328,696 \|
	\| `model-00005-of-00007.safetensors` \| `f815eef9e6ed5194fdc0ac7412ad29019661ca6062bcd454fb4ce341834e29a4` \| 4,955,596,064 \|
	\| `model-00006-of-00007.safetensors` \| `d2de7016e251a68f0a7ba2c49615c243c20ad87dfaacca252d6b2c0277f5fbbb` \| 4,071,440,632 \|
	\| `model-00007-of-00007.safetensors` \| `1bed352a0625d22dc606de47fd3563f24fd9fa73915c4ddffe7947c4b56c28bf` \| 3,019,999,168 \|

	## Notes

	- The adapter (`adapter/`) remains the numerically exact training artifact; the merged checkpoint adds only the measured re-quantization error above.
	- Probes ran on the training stack runtime (identical kernels to the serving configuration used for evaluation).