Instructions to use AnchorSR/AnchorSR-InternVL3.5-8B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AnchorSR/AnchorSR-InternVL3.5-8B-SFT with Transformers:

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

pipe = pipeline("image-text-to-text", model="AnchorSR/AnchorSR-InternVL3.5-8B-SFT")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("AnchorSR/AnchorSR-InternVL3.5-8B-SFT")
model = AutoModelForMultimodalLM.from_pretrained("AnchorSR/AnchorSR-InternVL3.5-8B-SFT", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AnchorSR/AnchorSR-InternVL3.5-8B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AnchorSR/AnchorSR-InternVL3.5-8B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AnchorSR/AnchorSR-InternVL3.5-8B-SFT",
		"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/AnchorSR/AnchorSR-InternVL3.5-8B-SFT

SGLang

How to use AnchorSR/AnchorSR-InternVL3.5-8B-SFT 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 "AnchorSR/AnchorSR-InternVL3.5-8B-SFT" \
    --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": "AnchorSR/AnchorSR-InternVL3.5-8B-SFT",
		"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 "AnchorSR/AnchorSR-InternVL3.5-8B-SFT" \
        --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": "AnchorSR/AnchorSR-InternVL3.5-8B-SFT",
		"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 AnchorSR/AnchorSR-InternVL3.5-8B-SFT with Docker Model Runner:
```
docker model run hf.co/AnchorSR/AnchorSR-InternVL3.5-8B-SFT
```

AnchorSR-InternVL3.5-8B-SFT

Full-parameter supervised fine-tuning of InternVL3.5-8B-HF on AnchorSR v3.

Training provenance

Run: internvl3_5_8b_sft_20260908_120038_665121
Training examples: 32,000; validation examples: 8,000.
Completed epochs: 2.
Optimizer updates: 2000 / 2000.
Actual global batch size for this completed run: 32.
Precision / distributed optimizer: BF16 / DeepSpeed ZeRO-2.
Video sampling: uniform 16 frames.
Numeric-answer loss share: 0.2.
Final validation loss: 0.14299454.

The validation loss is a training metric, not a task benchmark score; losses across different model tokenizers are not directly comparable.

Final model weights, tokenizer, processor and generation configuration are included. Intermediate checkpoints and optimizer states are not included. This export supports model loading and inference; it is not an exact optimizer resume checkpoint. Preserve the supplied chat template and multimodal processor.

This model is intended for research on spatial reasoning. Downstream benchmark accuracy and general deployment behavior have not been established by these training-completion checks.

Downloads last month: -

Safetensors

Model size

9B params

Tensor type

BF16

AnchorSR
/

AnchorSR-InternVL3.5-8B-SFT

AnchorSR-InternVL3.5-8B-SFT

Training provenance

Contents

Dataset used to train AnchorSR/AnchorSR-InternVL3.5-8B-SFT