Instructions to use lmsdss/SAVER-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lmsdss/SAVER-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="lmsdss/SAVER-2B")
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("lmsdss/SAVER-2B")
model = AutoModelForMultimodalLM.from_pretrained("lmsdss/SAVER-2B", 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 lmsdss/SAVER-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lmsdss/SAVER-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmsdss/SAVER-2B",
		"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/lmsdss/SAVER-2B

SGLang

How to use lmsdss/SAVER-2B 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 "lmsdss/SAVER-2B" \
    --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": "lmsdss/SAVER-2B",
		"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 "lmsdss/SAVER-2B" \
        --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": "lmsdss/SAVER-2B",
		"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 lmsdss/SAVER-2B with Docker Model Runner:
```
docker model run hf.co/lmsdss/SAVER-2B
```

Update pipeline tag, add license and links

by nielsr HF Staff - opened 3 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+14

-13

Files changed (1) hide show

README.md +14 -13

README.md CHANGED Viewed

@@ -1,15 +1,16 @@
 ---
-library_name: transformers
 base_model: Qwen/Qwen3.5-2B
-base_model_relation: finetune
-pipeline_tag: image-text-to-text
 tags:
-  - SAVER
-  - video
-  - video-question-answering
-  - temporal-grounding
-  - reinforcement-learning
-  - qwen3_5
 ---
 # SAVER 2B
@@ -54,8 +55,8 @@ model = Qwen3_5ForConditionalGeneration.from_pretrained(
 This model derives from Qwen3.5-2B. The upstream base model is distributed
 under Apache 2.0; its license and attribution requirements continue to apply.
-## Paper
-**Less from More: Reinforcing Sparse Video Reasoning from Dense References**
-A paper URL and formal citation will be added when available.

 ---
 base_model: Qwen/Qwen3.5-2B
+library_name: transformers
+pipeline_tag: video-text-to-text
+license: apache-2.0
 tags:
+- SAVER
+- video
+- video-question-answering
+- temporal-grounding
+- reinforcement-learning
+- qwen3_5
+base_model_relation: finetune
 ---
 # SAVER 2B
 This model derives from Qwen3.5-2B. The upstream base model is distributed
 under Apache 2.0; its license and attribution requirements continue to apply.
+## Links
+- **Paper:** [Less from More: Reinforcing Sparse Video Reasoning from Dense References](https://huggingface.co/papers/2610.10893)
+- **Project page:** [https://wenfangsun.cn/SAVER/](https://wenfangsun.cn/SAVER/)
+- **Code:** [https://github.com/lmsdss/SAVER](https://github.com/lmsdss/SAVER)