wangkanai / qwen2.5-vl-7b-instruct

Qwen2.5-VL-7B-Instruct

Qwen2.5-VL-7B-Instruct is a vision-language model from the Qwen 2.5 family, designed for multimodal understanding and generation tasks. This 7-billion parameter model can process both images and text, making it suitable for visual question answering, image captioning, and multimodal conversational AI.

Model Description

Qwen2.5-VL-7B-Instruct is an instruction-tuned multimodal large language model that combines:

• Vision Understanding : Process and analyze images with high accuracy
• Language Generation : Generate coherent, contextually relevant text responses
• Instruction Following : Fine-tuned to follow user instructions effectively
• Multimodal Reasoning : Understand relationships between visual and textual information

Capabilities

• Visual Question Answering (VQA)
• Image Captioning and Description
• Optical Character Recognition (OCR)
• Chart and Diagram Understanding
• Multimodal Conversational AI
• Image-to-Text Tasks

Repository Contents

Note : This directory is prepared for Qwen2.5-VL-7B-Instruct model files. Expected files after download:

qwen2.5-vl-7b-instruct/
├── model-*.safetensors      # Model weights (multiple shards, ~14-16GB total)
├── config.json              # Model configuration
├── generation_config.json   # Generation parameters
├── preprocessor_config.json # Image preprocessing config
├── tokenizer.json           # Tokenizer vocabulary
├── tokenizer_config.json    # Tokenizer configuration
├── special_tokens_map.json  # Special tokens mapping
└── README.md                # This file

Expected Total Size : ~14-16 GB (FP16 precision)

Hardware Requirements

Minimum Requirements

• VRAM : 16 GB (FP16 inference)
• RAM : 32 GB system memory
• Disk Space : 20 GB (model + cache)
• GPU : NVIDIA GPU with CUDA support (RTX 3090, RTX 4090, A100, etc.)

Recommended Requirements

• VRAM : 24 GB or higher for comfortable inference
• RAM : 64 GB for optimal performance
• GPU : A100 (40GB/80GB) or H100 for production use

Optimization Options

• FP16 : ~14-16 GB VRAM (standard precision)
• INT8 Quantization : ~8-10 GB VRAM (reduced quality)
• INT4 Quantization : ~5-7 GB VRAM (further reduced quality)

Usage Examples

Basic Usage with Transformers

from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from PIL import Image

# Load model and processor
model_path = "E:/huggingface/qwen2.5-vl-7b-instruct"
model = Qwen2VLForConditionalGeneration.from_pretrained(
    model_path,
    torch_dtype="auto",
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_path)

# Load and process image
image = Image.open("example.jpg")
prompt = "Describe this image in detail."

# Prepare inputs
inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")

# Generate response
output = model.generate(**inputs, max_new_tokens=256)
response = processor.decode(output[0], skip_special_tokens=True)
print(response)

Visual Question Answering

# Ask questions about an image
image = Image.open("chart.png")
question = "What is the trend shown in this chart?"

inputs = processor(text=question, images=image, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=512)
answer = processor.decode(output[0], skip_special_tokens=True)
print(f"Answer: {answer}")

Batch Processing Multiple Images

from pathlib import Path

# Process multiple images
image_dir = Path("E:/images")
images = [Image.open(img) for img in image_dir.glob("*.jpg")]
prompts = ["Describe this image."] * len(images)

# Batch processing
inputs = processor(text=prompts, images=images, return_tensors="pt", padding=True).to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)

for idx, output in enumerate(outputs):
    response = processor.decode(output, skip_special_tokens=True)
    print(f"Image {idx+1}: {response}")

Conversational Mode

# Multi-turn conversation about an image
image = Image.open("scene.jpg")
conversation = [
    {"role": "user", "content": "What do you see in this image?"},
]

# First turn
inputs = processor(text=conversation[0]["content"], images=image, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=256)
assistant_response = processor.decode(output[0], skip_special_tokens=True)

# Continue conversation
conversation.append({"role": "assistant", "content": assistant_response})
conversation.append({"role": "user", "content": "What color is the main object?"})

# Build conversation prompt
full_prompt = "\n".join([f"{msg['role']}: {msg['content']}" for msg in conversation])
inputs = processor(text=full_prompt, images=image, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=256)
response = processor.decode(output[0], skip_special_tokens=True)
print(response)

Model Specifications

Architecture

• Model Type : Vision-Language Model (VLM)
• Base Architecture : Qwen2.5 transformer with vision encoder
• Parameters : 7 billion
• Vision Encoder : Integrated vision transformer
• Context Length : 32,768 tokens (text + image tokens)
• Precision : FP16 (default), supports INT8/INT4 quantization

Training Details

• Instruction Tuning : Fine-tuned for instruction following
• Multimodal Training : Joint training on image-text pairs
• Language Support : Primarily English, with multilingual capabilities

Performance Characteristics

• Inference Speed : ~20-30 tokens/second (FP16, A100)
• Image Resolution : Flexible, typically 224x224 to 448x448
• Batch Processing : Supports batch inference for efficiency

Performance Tips and Optimization

Memory Optimization

# Use 8-bit quantization for reduced VRAM
from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0
)

model = Qwen2VLForConditionalGeneration.from_pretrained(
    model_path,
    quantization_config=quantization_config,
    device_map="auto"
)

Inference Speed

# Enable Flash Attention 2 for faster inference
model = Qwen2VLForConditionalGeneration.from_pretrained(
    model_path,
    torch_dtype="auto",
    device_map="auto",
    attn_implementation="flash_attention_2"  # Requires flash-attn package
)

Generation Parameters

# Optimize generation settings
generation_config = {
    "max_new_tokens": 256,
    "temperature": 0.7,
    "top_p": 0.9,
    "top_k": 50,
    "do_sample": True,
    "repetition_penalty": 1.1
}

output = model.generate(**inputs, **generation_config)

Best Practices

1. Image Preprocessing : Resize images to optimal resolution (448x448) before processing
2. Batch Processing : Process multiple images in batches for efficiency
3. Prompt Engineering : Use clear, specific prompts for better results
4. Temperature Tuning : Lower temperature (0.1-0.3) for factual tasks, higher (0.7-1.0) for creative tasks
5. Memory Management : Clear CUDA cache between large batches with torch.cuda.empty_cache()

Installation Requirements

pip install transformers>=4.37.0
pip install torch>=2.0.0
pip install torchvision
pip install pillow
pip install accelerate
pip install sentencepiece

# Optional: for quantization
pip install bitsandbytes

# Optional: for faster inference
pip install flash-attn --no-build-isolation

License

This model is released under the Apache License 2.0 .

You are free to:

• Use the model for commercial and non-commercial purposes
• Modify and distribute the model
• Use the model in proprietary applications

Conditions :

• Provide attribution to the original authors
• Include a copy of the license
• State any significant changes made to the model

See the full license text at: https://www.apache.org/licenses/LICENSE-2.0

Citation

If you use this model in your research or applications, please cite:

@article{qwen2.5-vl,
  title={Qwen2.5-VL: A Vision-Language Model Family},
  author={Qwen Team},
  journal={arXiv preprint},
  year={2024},
  url={https://github.com/QwenLM/Qwen2.5-VL}
}

Links and Resources

• Official Repository : https://github.com/QwenLM/Qwen2.5-VL
• Hugging Face Model : https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct
• Documentation : https://qwen.readthedocs.io/
• Paper : [Link to technical report when available]
• Demo : https://huggingface.co/spaces/Qwen/Qwen2.5-VL-7B-Instruct

Support and Contact

• Issues : Report issues on GitHub: https://github.com/QwenLM/Qwen2.5-VL/issues
• Community : Join discussions on Hugging Face: https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct/discussions
• Updates : Follow Qwen team for model updates and announcements

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Model Status : Ready for download and deployment Last Updated : 2025-10-28 README Version : v1.0