Instructions to use lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct", trust_remote_code=True)
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)

# Load model directly
from transformers import AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct

SGLang

How to use lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct 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 "lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct" \
    --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": "lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct" \
        --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": "lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct
```

Update processing_llava_onevision2.py

by chengcz - opened 4 days ago

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-3

Files changed (1) hide show

processing_llava_onevision2.py +7 -3

processing_llava_onevision2.py CHANGED Viewed

@@ -116,17 +116,21 @@ class LlavaOnevision2Processor:
         # Drop kwargs that AutoProcessor injects but downstream constructors
         # don't accept (e.g. _from_auto / trust_remote_code propagation).
         kwargs.pop("_from_auto", None)
-        kwargs.pop("trust_remote_code", None)
         kwargs.pop("code_revision", None)
         codec_config_override = kwargs.pop("codec_config", None)
         # Use the SLOW Qwen2VLImageProcessor: the Fast variant has small
         # normalization rounding differences that change pixel_values bit-for-bit.
         image_processor = Qwen2VLImageProcessor.from_pretrained(
-            pretrained_model_name_or_path, **kwargs
         )
         tokenizer = AutoTokenizer.from_pretrained(
-            pretrained_model_name_or_path, **kwargs
         )
         # Use the bundled VideoProcessor. Try a relative import first (when

         # Drop kwargs that AutoProcessor injects but downstream constructors
         # don't accept (e.g. _from_auto / trust_remote_code propagation).
         kwargs.pop("_from_auto", None)
+        trust_remote_code = kwargs.pop("trust_remote_code", None)
         kwargs.pop("code_revision", None)
         codec_config_override = kwargs.pop("codec_config", None)
         # Use the SLOW Qwen2VLImageProcessor: the Fast variant has small
         # normalization rounding differences that change pixel_values bit-for-bit.
         image_processor = Qwen2VLImageProcessor.from_pretrained(
+            pretrained_model_name_or_path,
+            trust_remote_code = trust_remote_code,
+            **kwargs,
         )
         tokenizer = AutoTokenizer.from_pretrained(
+            pretrained_model_name_or_path,
+            trust_remote_code = trust_remote_code,
+            **kwargs,
         )
         # Use the bundled VideoProcessor. Try a relative import first (when