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

Name: qwen2.5-vl-7b-instruct
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Qwen2.5-VL-7B-Instruct (Abliterated)

Qwen2.5-VL-7B-Instruct is a vision-language model from the Qwen 2.5 family, designed for multimodal understanding and generation tasks. This is an abliterated version with safety filters reduced or removed, providing more direct responses. The 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
Abliterated Version: Modified to reduce refusal behaviors and safety restrictions

Capabilities

Visual Question Answering (VQA)
Image Captioning and Description
Optical Character Recognition (OCR)
Chart and Diagram Understanding
Multimodal Conversational AI
Image-to-Text Tasks
Uncensored responses for research and creative applications

Repository Contents

qwen2.5-vl-7b-instruct/
├── qwen2.5-vl-7b-instruct-abliterated.safetensors       # 16GB (FP16 SafeTensors)
├── qwen2.5-vl-7b-instruct-abliterated-f16.gguf          # 15GB (FP16 GGUF)
├── qwen2.5-vl-7b-instruct-abliterated-q5-k-m.gguf       # 5.1GB (Q5_K_M quantized)
└── qwen2.5-vl-7b-instruct-abliterated-q4-k-m.gguf       # 4.4GB (Q4_K_M quantized)

Total Repository Size: ~40GB

Format Descriptions

SafeTensors (FP16): Full precision format for transformers/diffusers libraries (16GB)
GGUF F16: Full precision GGUF format for llama.cpp and compatible runtimes (15GB)
GGUF Q5_K_M: 5-bit mixed quantization balancing quality and size (5.1GB)
GGUF Q4_K_M: 4-bit mixed quantization for maximum efficiency (4.4GB)

Hardware Requirements

Format	VRAM Required	Disk Space	Recommended GPU
FP16 SafeTensors	~16-18GB	16GB	RTX 4090, A100, A6000
FP16 GGUF	~15-16GB	15GB	RTX 4090, A100, A6000
Q5_K_M GGUF	~6-7GB	5.1GB	RTX 3090, RTX 4070 Ti, V100
Q4_K_M GGUF	~5-6GB	4.4GB	RTX 3060 12GB, RTX 4060 Ti

System Requirements:

CPU: Modern multi-core processor (8+ cores recommended)
RAM: 16GB minimum, 32GB recommended
Storage: SSD recommended for faster model loading
OS: Windows, Linux, or macOS with CUDA support (NVIDIA GPUs)

Usage Examples

Using with Transformers (SafeTensors)

python

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

# Load model and processor
model_path = "E:/huggingface/qwen2.5-vl-7b-instruct"
model = Qwen2VLForConditionalGeneration.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")

# Load and process image
image = Image.open("your_image.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=512,
    temperature=0.7,
    do_sample=True
)

# Decode response
response = processor.decode(output[0], skip_special_tokens=True)
print(response)

Using with llama.cpp (GGUF)

bash

# Q4_K_M quantized version (most efficient)
llama-cli \
  --model "E:/huggingface/qwen2.5-vl-7b-instruct/qwen2.5-vl-7b-instruct-abliterated-q4-k-m.gguf" \
  --image "your_image.jpg" \
  --prompt "What do you see in this image?" \
  --ctx-size 4096 \
  --n-gpu-layers 35 \
  --temp 0.7

# Q5_K_M quantized version (better quality)
llama-cli \
  --model "E:/huggingface/qwen2.5-vl-7b-instruct/qwen2.5-vl-7b-instruct-abliterated-q5-k-m.gguf" \
  --image "your_image.jpg" \
  --prompt "Analyze the objects and their relationships in this image." \
  --ctx-size 4096 \
  --n-gpu-layers 35 \
  --temp 0.7

Using with Ollama

bash

# Create Modelfile
cat > Modelfile <<EOF
FROM E:/huggingface/qwen2.5-vl-7b-instruct/qwen2.5-vl-7b-instruct-abliterated-q4-k-m.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.9
EOF

# Import model
ollama create qwen2.5-vl-abliterated -f Modelfile

# Use the model
ollama run qwen2.5-vl-abliterated "Describe this image" --image your_image.jpg

Model Specifications

Specification	Details
Architecture	Qwen2.5-VL (Vision-Language Transformer)
Parameters	7 billion
Context Length	4096 tokens (text + image)
Vision Encoder	ViT-based image encoder
Precision	FP16 (full), Q5_K_M, Q4_K_M (quantized)
Formats	SafeTensors, GGUF
Modification	Abliterated (safety filters reduced)
Input Types	Text + Images (JPEG, PNG, WebP)
Output Type	Text (natural language)

Quantization Details

Q5_K_M: 5-bit quantization with mixed precision, ~66% size reduction, minimal quality loss
Q4_K_M: 4-bit quantization with mixed precision, ~71% size reduction, slight quality trade-off

Performance Tips

Optimization Strategies

Use Quantized Models: Q5_K_M or Q4_K_M formats provide excellent quality with lower VRAM usage
GPU Offloading: Use --n-gpu-layers parameter to maximize GPU utilization
Context Management: Keep context length reasonable (2048-4096) for faster inference
Batch Processing: Process multiple images in batches for efficiency
Temperature Control: Lower temperature (0.5-0.7) for factual descriptions, higher (0.8-1.0) for creative tasks

Memory Optimization

python

# Enable memory-efficient attention
model = Qwen2VLForConditionalGeneration.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    use_flash_attention_2=True,  # Requires flash-attn installed
    low_cpu_mem_usage=True
)

Image Preprocessing

python

from PIL import Image

# Resize large images to reduce memory usage
def preprocess_image(image_path, max_size=1024):
    image = Image.open(image_path)
    image.thumbnail((max_size, max_size), Image.LANCZOS)
    return image

Abliteration Notice

⚠️ Important: This is an abliterated model variant with reduced safety restrictions. Key considerations:

Uncensored Responses: The model may provide responses without typical safety refusals
Research Use: Primarily intended for research, creative applications, and controlled environments
Responsibility: Users are responsible for appropriate use and content filtering
No Guarantees: Abliteration does not guarantee complete removal of all safety behaviors
Legal Compliance: Ensure usage complies with local laws and regulations

License

This model is licensed under Apache 2.0, allowing both commercial and non-commercial use with attribution.

License Terms

✅ Commercial use permitted
✅ Modification and redistribution allowed
✅ Patent use granted
⚠️ Liability and warranty disclaimers apply
📄 Must include license and copyright notice

See the Apache 2.0 license for full terms: https://www.apache.org/licenses/LICENSE-2.0

Citation

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

bibtex

@article{qwen2.5-vl,
  title={Qwen2.5-VL: A Vision-Language Model with Enhanced Capabilities},
  author={Qwen Team},
  journal={arXiv preprint},
  year={2024},
  url={https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct}
}

Resources and Links

Official Model: Qwen/Qwen2.5-VL-7B-Instruct
Qwen Documentation: https://github.com/QwenLM/Qwen2.5-VL
Transformers Library: https://github.com/huggingface/transformers
llama.cpp: https://github.com/ggerganov/llama.cpp
GGUF Format: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md

Technical Support

For technical issues or questions:

Model Issues: Check official Qwen repository issues
GGUF Format: Refer to llama.cpp documentation
Transformers: Consult Hugging Face transformers documentation
Quantization: Review GGUF quantization guides

Version History

v1.2 (2025-10-29): Corrected SafeTensors file size (16GB) and VRAM requirements
v1.1 (2025-10-29): Updated documentation with accurate file information and abliterated model details
v1.0 (2025-10-28): Initial README with Hugging Face metadata

Model Format: SafeTensors + GGUF | Precision: FP16, Q5_K_M, Q4_K_M | Size: 4.4GB - 16GB

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

Qwen2.5-VL-7B-Instruct (Abliterated)

Model Description

Capabilities

Repository Contents

Format Descriptions

Hardware Requirements

Usage Examples

Using with Transformers (SafeTensors)

Using with llama.cpp (GGUF)

Using with Ollama

Model Specifications

Quantization Details

Performance Tips

Optimization Strategies

Memory Optimization

Image Preprocessing

Abliteration Notice

License

License Terms

Citation

Resources and Links

Technical Support

Version History

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