Instructions to use ByteDance/Sa2VA-LLaVA-1.5-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ByteDance/Sa2VA-LLaVA-1.5-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="ByteDance/Sa2VA-LLaVA-1.5-7B", 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 AutoModel
model = AutoModel.from_pretrained("ByteDance/Sa2VA-LLaVA-1.5-7B", trust_remote_code=True, device_map="auto")

sam2

How to use ByteDance/Sa2VA-LLaVA-1.5-7B with sam2:

# Use SAM2 with images
import torch
from sam2.sam2_image_predictor import SAM2ImagePredictor

predictor = SAM2ImagePredictor.from_pretrained(ByteDance/Sa2VA-LLaVA-1.5-7B)

with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
    predictor.set_image(<your_image>)
    masks, _, _ = predictor.predict(<input_prompts>)

# Use SAM2 with videos
import torch
from sam2.sam2_video_predictor import SAM2VideoPredictor

predictor = SAM2VideoPredictor.from_pretrained(ByteDance/Sa2VA-LLaVA-1.5-7B)

with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
    state = predictor.init_state(<your_video>)

    # add new prompts and instantly get the output on the same frame
    frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>):

    # propagate the prompts to get masklets throughout the video
    for frame_idx, object_ids, masks in predictor.propagate_in_video(state):
        ...

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ByteDance/Sa2VA-LLaVA-1.5-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ByteDance/Sa2VA-LLaVA-1.5-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteDance/Sa2VA-LLaVA-1.5-7B",
		"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/ByteDance/Sa2VA-LLaVA-1.5-7B

SGLang

How to use ByteDance/Sa2VA-LLaVA-1.5-7B 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 "ByteDance/Sa2VA-LLaVA-1.5-7B" \
    --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": "ByteDance/Sa2VA-LLaVA-1.5-7B",
		"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 "ByteDance/Sa2VA-LLaVA-1.5-7B" \
        --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": "ByteDance/Sa2VA-LLaVA-1.5-7B",
		"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 ByteDance/Sa2VA-LLaVA-1.5-7B with Docker Model Runner:
```
docker model run hf.co/ByteDance/Sa2VA-LLaVA-1.5-7B
```

Sa2VA-LLaVA-1.5-7B / config.json

HarborYuan

Add Sa2VA-LLaVA-1.5-7B (full RefCOCO/+/g + VOS + GCG eval)

aec4f5b verified 4 months ago

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	{
	"architectures": [
	"Sa2VAChatModelLlava"
	],
	"auto_map": {
	"AutoConfig": "configuration_sa2va_chat.Sa2VAChatConfigLlava",
	"AutoModel": "modeling_sa2va_llava.Sa2VAChatModelLlava",
	"AutoModelForCausalLM": "modeling_sa2va_llava.Sa2VAChatModelLlava"
	},
	"dtype": "float32",
	"ignore_index": -100,
	"image_seq_length": 576,
	"image_token_index": 32000,
	"model_type": "sa2va_chat",
	"multimodal_projector_bias": true,
	"pad_token_id": 32001,
	"projector_hidden_act": "gelu",
	"template": "vicuna",
	"text_config": {
	"_name_or_path": "lmsys/vicuna-7b-v1.5",
	"architectures": [
	"LlamaForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"dtype": "float16",
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 4096,
	"initializer_range": 0.02,
	"intermediate_size": 11008,
	"max_position_embeddings": 4096,
	"mlp_bias": false,
	"model_type": "llama",
	"num_attention_heads": 32,
	"num_hidden_layers": 32,
	"num_key_value_heads": 32,
	"pretraining_tp": 1,
	"rms_norm_eps": 1e-05,
	"rope_scaling": null,
	"rope_theta": 10000.0,
	"use_cache": true,
	"vocab_size": 32007
	},
	"tie_word_embeddings": false,
	"transformers_version": "4.57.1",
	"vision_config": {
	"attention_dropout": 0.0,
	"hidden_act": "quick_gelu",
	"hidden_size": 1024,
	"image_size": 336,
	"initializer_factor": 1.0,
	"initializer_range": 0.02,
	"intermediate_size": 4096,
	"layer_norm_eps": 1e-05,
	"model_type": "clip_vision_model",
	"num_attention_heads": 16,
	"num_channels": 3,
	"num_hidden_layers": 24,
	"patch_size": 14,
	"projection_dim": 768,
	"vocab_size": 32000
	},
	"vision_feature_layer": -2,
	"vision_feature_select_strategy": "default",
	"vocab_size": 32064
	}