Qwen2.5 VL 32B Instruct FP8 Dynamic

🚀 Qwen2.5-VL-32B-Instruct-FP8-Dynamic

This is a quantized version of Qwen/Qwen2.5-VL-32B-Instruct. It offers optimized performance through specific quantization techniques and is well - suited for vision - text input scenarios.

✨ Features

Model Overview

• Model Architecture: Qwen2.5-VL-72B-Instruct
  • Input: Vision - Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: FP8
  • Activation quantization: FP8
• Release Date: 2/24/2025
• Version: 1.0
• Model Developers: Neural Magic

Model Optimizations

This model was obtained by quantizing the weights of Qwen/Qwen2.5-VL-32B-Instruct to the FP8 data type. It is ready for inference with vLLM >= 0.5.2.

📦 Installation

No specific installation steps were provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

from vllm.assets.image import ImageAsset
from vllm import LLM, SamplingParams

# prepare model
llm = LLM(
    model="neuralmagic/Qwen2.5-VL-72B-Instruct-FP8-Dynamic",
    trust_remote_code=True,
    max_model_len=4096,
    max_num_seqs=2,
)

# prepare inputs
question = "What is the content of this image?"
inputs = {
    "prompt": f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n",
    "multi_modal_data": {
        "image": ImageAsset("cherry_blossom").pil_image.convert("RGB")
    },
}

# generate response
print("========== SAMPLE GENERATION ==============")
outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64))
print(f"PROMPT  : {outputs[0].prompt}")
print(f"RESPONSE: {outputs[0].outputs[0].text}")
print("==========================================")

vLLM also supports OpenAI - compatible serving. See the documentation for more details.

📚 Documentation

No detailed documentation other than the usage example was provided in the original document, so this section is skipped.

🔧 Technical Details

No specific technical details were provided in the original document, so this section is skipped.

📄 License

This model is licensed under the Apache 2.0 license.

Information Table