Instructions to use lmms-lab/LLaVA-Video-72B-Qwen2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lmms-lab/LLaVA-Video-72B-Qwen2 with Transformers:

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

pipe = pipeline("text-generation", model="lmms-lab/LLaVA-Video-72B-Qwen2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("lmms-lab/LLaVA-Video-72B-Qwen2")
model = AutoModelForCausalLM.from_pretrained("lmms-lab/LLaVA-Video-72B-Qwen2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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 lmms-lab/LLaVA-Video-72B-Qwen2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lmms-lab/LLaVA-Video-72B-Qwen2"
# 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/LLaVA-Video-72B-Qwen2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lmms-lab/LLaVA-Video-72B-Qwen2

SGLang

How to use lmms-lab/LLaVA-Video-72B-Qwen2 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/LLaVA-Video-72B-Qwen2" \
    --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/LLaVA-Video-72B-Qwen2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/LLaVA-Video-72B-Qwen2" \
        --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/LLaVA-Video-72B-Qwen2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lmms-lab/LLaVA-Video-72B-Qwen2 with Docker Model Runner:
```
docker model run hf.co/lmms-lab/LLaVA-Video-72B-Qwen2
```

ZhangYuanhan commited on Sep 26, 2024

Commit

3c8d458

verified ·

1 Parent(s): bf1fb81

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -9

README.md CHANGED Viewed

@@ -144,7 +144,9 @@ This model support at most 64 frames.
 ### Intended use
-The model was trained on [LLaVA-Video-178K](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Video-SFT-Data) and [LLaVA-OneVision Dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data), having  have the ability to interact with images, multi-image and videos, but specific to videos.
 **Feel free to share your generations in the Community tab!**
@@ -166,9 +168,7 @@ import sys
 import warnings
 from decord import VideoReader, cpu
 import numpy as np
 warnings.filterwarnings("ignore")
 def load_video(self, video_path, max_frames_num,fps=1,force_sample=False):
     if max_frames_num == 0:
         return np.zeros((1, 336, 336, 3))
@@ -186,14 +186,12 @@ def load_video(self, video_path, max_frames_num,fps=1,force_sample=False):
     frame_time = ",".join([f"{i:.2f}s" for i in frame_time])
     spare_frames = vr.get_batch(frame_idx).asnumpy()
     # import pdb;pdb.set_trace()
     return spare_frames,frame_time,video_time
 pretrained = "lmms-lab/LLaVA-NeXT-Video-72B-Qwen2"
 model_name = "llava_qwen"
 device = "cuda"
 device_map = "auto"
-tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map)  # Add any other thing you want to pass in llava_model_args
 model.eval()
 video_path = "XXXX"
 max_frames_num = "64"
@@ -201,7 +199,8 @@ video,frame_time,video_time = load_video(video_path, max_frames_num, 1, force_sa
 video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].cuda().bfloat16()
 video = [video]
 conv_template = "qwen_1_5"  # Make sure you use correct chat template for different models
-question = DEFAULT_IMAGE_TOKEN + "\nPlease describe this video in detail."
 conv = copy.deepcopy(conv_templates[conv_template])
 conv.append_message(conv.roles[0], question)
 conv.append_message(conv.roles[1], None)
@@ -210,12 +209,12 @@ input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX,
 cont = model.generate(
     input_ids,
     images=video,
-    modalities=["video"],
     do_sample=False,
     temperature=0,
     max_new_tokens=4096,
 )
-text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
 print(text_outputs)
 ```

 ### Intended use
+The model was trained on [LLaVA-Video-178K](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Video-SFT-Data) and [LLaVA-OneVision Dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data), having the ability to interact with images, multi-image and videos, but specific to videos.
 **Feel free to share your generations in the Community tab!**
 import warnings
 from decord import VideoReader, cpu
 import numpy as np
 warnings.filterwarnings("ignore")
 def load_video(self, video_path, max_frames_num,fps=1,force_sample=False):
     if max_frames_num == 0:
         return np.zeros((1, 336, 336, 3))
     frame_time = ",".join([f"{i:.2f}s" for i in frame_time])
     spare_frames = vr.get_batch(frame_idx).asnumpy()
     # import pdb;pdb.set_trace()
     return spare_frames,frame_time,video_time
 pretrained = "lmms-lab/LLaVA-NeXT-Video-72B-Qwen2"
 model_name = "llava_qwen"
 device = "cuda"
 device_map = "auto"
+tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map)  # Add any other thing you want to pass in llava_model_args
 model.eval()
 video_path = "XXXX"
 max_frames_num = "64"
 video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].cuda().bfloat16()
 video = [video]
 conv_template = "qwen_1_5"  # Make sure you use correct chat template for different models
+time_instruciton = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. These frames are located at {frame_time}.Please answer the following questions related to this video."
+question = DEFAULT_IMAGE_TOKEN + f"{time_instruciton}\nPlease describe this video in detail."
 conv = copy.deepcopy(conv_templates[conv_template])
 conv.append_message(conv.roles[0], question)
 conv.append_message(conv.roles[1], None)
 cont = model.generate(
     input_ids,
     images=video,
+    modalities= ["video"],
     do_sample=False,
     temperature=0,
     max_new_tokens=4096,
 )
+text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)[0].strip()
 print(text_outputs)
 ```