Instructions to use LibraCaption/InternVL3-38B-Captioner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LibraCaption/InternVL3-38B-Captioner with Transformers:

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

pipe = pipeline("image-text-to-text", model="LibraCaption/InternVL3-38B-Captioner")
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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("LibraCaption/InternVL3-38B-Captioner")
model = AutoModelForImageTextToText.from_pretrained("LibraCaption/InternVL3-38B-Captioner")
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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LibraCaption/InternVL3-38B-Captioner with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LibraCaption/InternVL3-38B-Captioner"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LibraCaption/InternVL3-38B-Captioner",
		"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/LibraCaption/InternVL3-38B-Captioner

SGLang

How to use LibraCaption/InternVL3-38B-Captioner 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 "LibraCaption/InternVL3-38B-Captioner" \
    --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": "LibraCaption/InternVL3-38B-Captioner",
		"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 "LibraCaption/InternVL3-38B-Captioner" \
        --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": "LibraCaption/InternVL3-38B-Captioner",
		"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 LibraCaption/InternVL3-38B-Captioner with Docker Model Runner:
```
docker model run hf.co/LibraCaption/InternVL3-38B-Captioner
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

sft_captioner

This model is a fine-tuned version of OpenGVLab/InternVL3-38B-hf on the pyq_part1_captioner_0815, the pyq_part2_captioner_0815 and the private_captioner_0815_optimized.json datasets. It achieves the following results on the evaluation set:

Loss: 0.7323

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 4
eval_batch_size: 4
seed: 2025
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 32
total_eval_batch_size: 32
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.8966	0.0868	500	0.8954
0.861	0.1735	1000	0.8705
0.8622	0.2603	1500	0.8549
0.82	0.3470	2000	0.8349
0.8211	0.4338	2500	0.8202
0.7978	0.5206	3000	0.8064
0.7955	0.6073	3500	0.7945
0.7845	0.6941	4000	0.7838
0.7617	0.7808	4500	0.7729
0.7772	0.8676	5000	0.7622
0.7641	0.9544	5500	0.7544
0.6061	1.0411	6000	0.7635
0.5863	1.1279	6500	0.7613
0.5777	1.2146	7000	0.7588
0.5943	1.3014	7500	0.7490
0.5816	1.3882	8000	0.7469
0.5723	1.4749	8500	0.7421
0.5721	1.5617	9000	0.7374
0.5724	1.6484	9500	0.7353
0.5731	1.7352	10000	0.7343
0.5597	1.8220	10500	0.7330
0.5731	1.9087	11000	0.7326
0.5557	1.9955	11500	0.7323

Framework versions

Transformers 4.52.4
Pytorch 2.7.1+cu126
Datasets 3.6.0
Tokenizers 0.21.1

Downloads last month: 22

Safetensors

Model size

38B params

Tensor type

BF16

Model tree for LibraCaption/InternVL3-38B-Captioner

Base model

OpenGVLab/InternVL3-38B-Pretrained

Finetuned

OpenGVLab/InternVL3-38B-Instruct

Finetuned

OpenGVLab/InternVL3-38B-hf

Finetuned

(1)

this model