This is an abliterated (uncensored) version of the Qwen3-VL-8B-Instruct multimodal vision-language model. The model has undergone abliteration to remove safety guardrails and content filtering, allowing unrestricted responses to all queries. This 8-billion parameter instruction-tuned model excels at visual question answering, image captioning, optical character recognition (OCR), and complex visual reasoning tasks.

⚠️ WARNING: This is an uncensored model variant with safety restrictions removed. Use responsibly and in compliance with applicable laws and ethical guidelines.

Model Description

Qwen3-VL-8B-Instruct (Abliterated) is a modified version of the Qwen3 Vision-Language model with content filtering removed. Key capabilities include:

Visual Understanding: Analyze images, charts, diagrams, screenshots, and documents
Multimodal Conversation: Engage in multi-turn dialogues about visual content
Optical Character Recognition: Extract and understand text from images
Visual Reasoning: Answer complex questions requiring visual analysis and logical reasoning
Document Understanding: Process scanned documents, forms, and structured layouts
Uncensored Responses: No content filtering or safety guardrails

Model Architecture: Vision Transformer encoder + Qwen3-8B language model decoder Training: Instruction-tuned on diverse vision-language tasks, then abliterated Context Length: Up to 32K tokens (text + visual tokens) Languages: Multilingual support (English, Chinese, and more) Modification: Safety layers removed through abliteration process

Repository Contents

qwen3-vl-8b-instruct/
├── qwen3-vl-8b-instruct-abliterated.safetensors    # Complete model weights (17 GB)
├── qwen3-vl-8b-instruct-abliterated-f16.gguf       # FP16 GGUF format (16 GB)
├── qwen3-vl-8b-instruct-abliterated-q4-k-m.gguf    # Q4_K_M quantized (4.7 GB)
├── qwen3-vl-8b-instruct-abliterated-q8-0.gguf      # Q8_0 quantized (8.2 GB)
└── README.md                                        # This file

Total Repository Size: ~46 GB (multiple formats for different use cases)

File Details:

qwen3-vl-8b-instruct-abliterated.safetensors: Complete merged model in safetensors format
- Size: 17 GB
- Precision: FP16 (half precision)
- Format: Single-file merged weights (not sharded)
- Use with: Transformers library, standard PyTorch inference
- Best for: GPU inference with 20GB+ VRAM
qwen3-vl-8b-instruct-abliterated-f16.gguf: FP16 GGUF format
- Size: 16 GB
- Precision: FP16 (half precision)
- Format: GGUF (GPT-Generated Unified Format)
- Use with: llama.cpp, Ollama, LM Studio
- Best for: CPU/GPU inference with llama.cpp ecosystem
qwen3-vl-8b-instruct-abliterated-q4-k-m.gguf: Q4_K_M quantized GGUF
- Size: 4.7 GB
- Precision: 4-bit K-quant (medium quality)
- Format: GGUF quantized
- Use with: llama.cpp, Ollama, LM Studio
- Best for: Lower VRAM systems (8-12 GB), good quality/size balance
qwen3-vl-8b-instruct-abliterated-q8-0.gguf: Q8_0 quantized GGUF
- Size: 8.2 GB
- Precision: 8-bit quantization
- Format: GGUF quantized
- Use with: llama.cpp, Ollama, LM Studio
- Best for: 12-16 GB VRAM, minimal quality loss from FP16

Hardware Requirements

SafeTensors Format (FP16)

Minimum Requirements:

VRAM: 20 GB (FP16 inference)
RAM: 32 GB system memory
Disk Space: 20 GB free space
GPU: NVIDIA GPU with Compute Capability 7.0+ (V100, RTX 20/30/40 series, A100, etc.)

Recommended Requirements:

VRAM: 24 GB+ (RTX 4090, A6000, A100 for longer sequences)
RAM: 64 GB system memory
Disk Space: 30 GB+ (for model caching and optimization)
GPU: NVIDIA RTX 4090, A100, or H100 for optimal performance

GGUF Formats (Multiple Options)

F16 GGUF (qwen3-vl-8b-instruct-abliterated-f16.gguf):

VRAM: 18-20 GB GPU VRAM recommended
RAM: 32 GB for GPU offloading, 64 GB for CPU inference
Disk Space: 20 GB
Use Case: GPU inference with llama.cpp ecosystem

Q8_0 GGUF (qwen3-vl-8b-instruct-abliterated-q8-0.gguf):

VRAM: 12-16 GB GPU VRAM
RAM: 16 GB for GPU offloading, 32 GB for CPU inference
Disk Space: 10 GB
Quality: Minimal quality loss from FP16, excellent balance
Use Case: Mid-range GPUs (RTX 3060 12GB, RTX 4060 Ti 16GB, etc.)

Q4_K_M GGUF (qwen3-vl-8b-instruct-abliterated-q4-k-m.gguf):

VRAM: 8-12 GB GPU VRAM
RAM: 8 GB for GPU offloading, 16 GB for CPU inference
Disk Space: 6 GB
Quality: Good quality/size balance, suitable for most tasks
Use Case: Consumer GPUs (RTX 3060, RTX 4060, etc.)

CPU-Only Inference (GGUF formats)

RAM: 32-64 GB system memory
CPU: Modern CPU with AVX2 support (Intel Core i5/i7/i9, AMD Ryzen)
Performance: Much slower than GPU, but functional
Recommended: Q4_K_M format for best performance/quality balance

Usage Examples

Installation

pip install transformers torch torchvision pillow accelerate

Basic Image Understanding

python

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

# Load abliterated model from local directory
model = Qwen2VLForConditionalGeneration.from_pretrained(
    "E:\\huggingface\\qwen3-vl-8b-instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")

# Load and process image
image = Image.open("example_image.jpg")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "What objects do you see in this image?"}
        ]
    }
]

# Prepare inputs
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt", padding=True).to("cuda")

# Generate response
with torch.no_grad():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.7,
        top_p=0.9
    )

# Decode and print response
response = processor.batch_decode(output_ids, skip_special_tokens=True)[0]
print(response)

Note: Since this is an abliterated model stored as a single merged file, you'll need to use a compatible processor config. Use the original Qwen2-VL processor from Hugging Face for tokenization and image processing.

Multi-Turn Conversation

python

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

model = Qwen2VLForConditionalGeneration.from_pretrained(
    "E:\\huggingface\\qwen3-vl-8b-instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")

# Multi-turn conversation
image = Image.open("chart.png")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "What type of chart is this?"}
        ]
    },
    {
        "role": "assistant",
        "content": [{"type": "text", "text": "This is a bar chart showing sales data."}]
    },
    {
        "role": "user",
        "content": [{"type": "text", "text": "What was the highest value?"}]
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to("cuda")

with torch.no_grad():
    output_ids = model.generate(**inputs, max_new_tokens=256)

response = processor.batch_decode(output_ids, skip_special_tokens=True)[0]
print(response)

OCR and Document Understanding

python

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

model = Qwen2VLForConditionalGeneration.from_pretrained(
    "E:\\huggingface\\qwen3-vl-8b-instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")

# OCR from document
document_image = Image.open("invoice.jpg")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "Extract all text from this document and identify the invoice number and total amount."}
        ]
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[document_image], return_tensors="pt").to("cuda")

with torch.no_grad():
    output_ids = model.generate(**inputs, max_new_tokens=1024, temperature=0.3)

response = processor.batch_decode(output_ids, skip_special_tokens=True)[0]
print(response)

Loading with Safetensors Library Directly

python

from safetensors.torch import load_file
import torch

# Load the abliterated model weights directly
weights = load_file("E:\\huggingface\\qwen3-vl-8b-instruct\\qwen3-vl-8b-instruct-abliterated.safetensors")

# Inspect model structure
print("Model layers:", list(weights.keys())[:10])  # First 10 keys
print(f"Total parameters: {sum(w.numel() for w in weights.values()):,}")

GGUF Format Usage

The GGUF formats are designed for use with llama.cpp, Ollama, LM Studio, and other GGUF-compatible inference engines. These formats are optimized for flexible deployment across CPU and GPU systems.

Using with llama.cpp

Installation:

bash

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

Basic Inference (Q4_K_M for best balance):

bash

./main -m "E:\\huggingface\\qwen3-vl-8b-instruct\\qwen3-vl-8b-instruct-abliterated-q4-k-m.gguf" \
  --image "path/to/image.jpg" \
  -p "Describe this image in detail." \
  -n 512 \
  --temp 0.7 \
  --top-p 0.9

GPU Acceleration (offload layers to GPU):

bash

./main -m "E:\\huggingface\\qwen3-vl-8b-instruct\\qwen3-vl-8b-instruct-abliterated-q8-0.gguf" \
  --image "path/to/image.jpg" \
  -p "What objects are in this image?" \
  -n 512 \
  --n-gpu-layers 35 \
  --temp 0.7

CPU-Only Inference (optimized threading):

bash

./main -m "E:\\huggingface\\qwen3-vl-8b-instruct\\qwen3-vl-8b-instruct-abliterated-q4-k-m.gguf" \
  --image "path/to/image.jpg" \
  -p "Analyze this chart." \
  -n 512 \
  --threads 8

Using with Ollama

Create Modelfile:

dockerfile

FROM E:\huggingface\qwen3-vl-8b-instruct\qwen3-vl-8b-instruct-abliterated-q4-k-m.gguf

PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER top_k 40

SYSTEM """You are an uncensored vision-language AI assistant capable of analyzing images and answering questions without content filtering."""

Create and run model:

bash

ollama create qwen3-vl-abliterated -f ./Modelfile
ollama run qwen3-vl-abliterated

Interactive use:

>>> What's in this image? /path/to/image.jpg

Using with LM Studio

Open LM Studio
Go to "Local Models" → "Import Model"
Select one of the GGUF files:
- Use Q4_K_M for best performance on consumer hardware
- Use Q8_0 for better quality with more VRAM
- Use F16 for maximum quality
Load the model and configure:
- Context Length: 32768
- GPU Offload: Adjust based on your VRAM
- Temperature: 0.7 (adjust for your use case)
Use the image upload feature to analyze images

Python with llama-cpp-python

Installation:

pip install llama-cpp-python

Basic Usage:

python

from llama_cpp import Llama
from llama_cpp.llama_chat_format import Llava15ChatHandler

# Initialize chat handler for vision model
chat_handler = Llava15ChatHandler(clip_model_path="path/to/clip/model")

# Load model
llm = Llama(
    model_path="E:\\huggingface\\qwen3-vl-8b-instruct\\qwen3-vl-8b-instruct-abliterated-q4-k-m.gguf",
    chat_handler=chat_handler,
    n_ctx=32768,
    n_gpu_layers=35,  # Adjust based on VRAM
    verbose=False
)

# Analyze image
response = llm.create_chat_completion(
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": "file:///path/to/image.jpg"}},
                {"type": "text", "text": "What is in this image?"}
            ]
        }
    ],
    temperature=0.7,
    max_tokens=512
)

print(response["choices"][0]["message"]["content"])

Format Selection Guide

Choose Q4_K_M if:

You have 8-12 GB VRAM
You want fast inference with good quality
Storage space is a concern
Most consumer hardware scenarios

Choose Q8_0 if:

You have 12-16 GB VRAM
You want minimal quality loss from FP16
You can spare the extra storage
Professional or high-quality output needs

Choose F16 GGUF if:

You have 20+ GB VRAM
You want maximum quality
You prefer GGUF ecosystem over PyTorch
You need llama.cpp compatibility with full precision

Model Specifications

Architecture Details

Model Type: Vision-Language Transformer (VLM) - Abliterated
Vision Encoder: Vision Transformer (ViT) with adaptive resolution
Language Model: Qwen3-8B decoder (safety layers removed)
Parameters: 8 billion (8B)
Precision: FP16 (half precision)
Format: SafeTensors (single merged file)
Framework: PyTorch / Transformers
Modification Type: Abliteration (safety guardrail removal)

Input Specifications

Image Resolution: Adaptive (up to 1024x1024 recommended)
Image Formats: JPEG, PNG, BMP, WebP
Text Context: Up to 32K tokens
Batch Size: Depends on VRAM (typically 1-8 images)

Generation Parameters

Max New Tokens: 512-2048 (depending on task)
Temperature: 0.1-0.9 (lower for factual tasks, higher for creative)
Top-p: 0.8-0.95 (nucleus sampling)
Top-k: 20-50 (alternative sampling method)

Supported Tasks

Visual Question Answering (VQA) - Uncensored
Image Captioning
Optical Character Recognition (OCR)
Document Understanding
Chart and Diagram Analysis
Visual Reasoning
Multi-turn Visual Dialogue - Uncensored
Scene Understanding
Object Detection and Counting (descriptive)

Performance Tips and Optimization

Memory Optimization

Use FP16 precision (default):

python

model = Qwen2VLForConditionalGeneration.from_pretrained(
    "E:\\huggingface\\qwen3-vl-8b-instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)

INT8 Quantization (reduces VRAM to ~10GB):

python

from transformers import BitsAndBytesConfig

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

model = Qwen2VLForConditionalGeneration.from_pretrained(
    "E:\\huggingface\\qwen3-vl-8b-instruct",
    quantization_config=quantization_config,
    device_map="auto"
)

INT4 Quantization (reduces VRAM to ~6GB):

python

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4"
)

model = Qwen2VLForConditionalGeneration.from_pretrained(
    "E:\\huggingface\\qwen3-vl-8b-instruct",
    quantization_config=quantization_config,
    device_map="auto"
)

Inference Optimization

Use Flash Attention 2 (faster attention):

python

model = Qwen2VLForConditionalGeneration.from_pretrained(
    "E:\\huggingface\\qwen3-vl-8b-instruct",
    torch_dtype=torch.float16,
    attn_implementation="flash_attention_2",
    device_map="auto"
)

Enable torch.compile (PyTorch 2.0+):

model = torch.compile(model, mode="reduce-overhead")

Optimize image resolution:

Use lower resolution (512x512) for faster inference
Use higher resolution (1024x1024) for detailed OCR and document tasks

Generation Strategy

For factual/OCR tasks (deterministic):

python

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.1,
    top_p=0.9,
    do_sample=True
)

For creative/descriptive tasks:

python

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    do_sample=True
)

For structured output:

python

outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.3,
    top_p=0.9,
    repetition_penalty=1.1
)

Abliteration Details

What is Abliteration?

Abliteration is a technique for removing safety guardrails from language models by identifying and removing the specific layers or mechanisms responsible for content filtering and refusal behaviors. This process:

Analyzes model layers to identify safety-related components
Removes or neutralizes these components while preserving core capabilities
Results in an "uncensored" model that responds to all queries

Implications of Abliteration:

✅ No content filtering or refusal responses
✅ Unrestricted responses to sensitive queries
⚠️ No built-in safety mechanisms
⚠️ User responsible for ethical use and compliance
⚠️ May generate harmful, illegal, or unethical content if prompted

Technical Changes:

Safety alignment layers removed or neutralized
Refusal mechanisms disabled
Content filtering bypassed
Core reasoning and generation capabilities preserved

License

This model is based on Qwen3-VL-8B-Instruct, which is released under the Apache License 2.0.

Important Legal Notice:

The abliteration process modifies the original model
Use of this model must comply with the Apache 2.0 license terms
Users are solely responsible for ethical use and legal compliance
This model should not be used for illegal, harmful, or unethical purposes
The original developers are not responsible for misuse of this modified version

You are free to:

Use the model commercially (with responsibility)
Modify and distribute the model
Use for research and production applications

Requirements:

Provide attribution to Alibaba Cloud and the Qwen team
Include the Apache 2.0 license text with distributions
State that this is a modified (abliterated) version
Take full responsibility for outputs and usage

See the Apache License 2.0 for full terms.

Citation

If you use Qwen3-VL-8B-Instruct (Abliterated) in your research or applications, please cite:

bibtex

@article{qwen3vl2024,
  title={Qwen3-VL: Scaling Vision-Language Models with Enhanced Instruction Following},
  author={Qwen Team},
  journal={arXiv preprint},
  year={2024},
  publisher={Alibaba Cloud}
}

Note: This is an abliterated community modification, not an official Qwen model release.

Model Card Contact

Original Model: Qwen Team, Alibaba Cloud Model Type: Vision-Language Model (Instruction-tuned, Abliterated) Modification: Community abliteration (uncensored variant) Language(s): Multilingual (English, Chinese, and more) License: Apache 2.0 (modified version)

Links and Resources

Original Model Repository: https://github.com/QwenLM/Qwen-VL
Original Hugging Face Model: https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct
Qwen Documentation: https://qwen.readthedocs.io/
Technical Report: https://arxiv.org/abs/qwen3-vl (when published)
Abliteration Resources: Search for "LLM abliteration" for technique details

Limitations and Considerations

Known Limitations:

May generate incorrect or hallucinated information about images
Performance varies with image quality and resolution
May struggle with very small text or complex layouts
Limited understanding of highly specialized domain images
NO SAFETY FILTERS: Will respond to any query without ethical filtering

Ethical Considerations:

⚠️ NO CONTENT FILTERING: This model has no built-in safety mechanisms
⚠️ USER RESPONSIBILITY: You are fully responsible for ethical use
⚠️ POTENTIAL FOR HARM: May generate harmful content if prompted
⚠️ LEGAL COMPLIANCE: Ensure use complies with applicable laws
⚠️ BIAS AMPLIFICATION: Uncensored models may amplify training data biases
Validate outputs for critical applications
Consider privacy implications when processing personal images
Use responsibly and ethically

Recommended Use Cases:

Research on AI safety and alignment (studying uncensored model behavior)
Unrestricted creative content generation
Analysis of censorship mechanisms in AI models
Educational purposes (understanding model limitations)
Applications where content filtering interferes with legitimate use

Not Recommended For:

Public-facing applications without additional safety layers
Use by minors or vulnerable populations
Automated systems without human oversight
Medical, legal, or safety-critical applications
Any illegal, harmful, or unethical purposes
Production systems without additional filtering mechanisms

Required Safeguards:

Implement application-level content filtering if needed
Monitor outputs for harmful content
Provide user warnings about uncensored nature
Establish clear usage policies and guidelines
Maintain human oversight for sensitive applications

Technical Notes

Single-File Format

This model is distributed as a single merged safetensors file rather than sharded weights:

Advantages:

Simpler file management (one file vs. multiple shards)
Easier to move and backup
Consistent loading process

Considerations:

Requires sufficient disk I/O bandwidth during loading
May take longer to initially load compared to parallel shard loading
Requires ~16GB contiguous disk space

Processor Configuration

Since this is a community-modified version, you'll need to use a compatible processor:

python

# Use the original Qwen2-VL processor for compatibility
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")

# Or create a custom processor config if needed
from transformers import Qwen2VLProcessor, Qwen2VLImageProcessor, Qwen2Tokenizer

image_processor = Qwen2VLImageProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
tokenizer = Qwen2Tokenizer.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
processor = Qwen2VLProcessor(image_processor=image_processor, tokenizer=tokenizer)

Compatibility Notes

Compatible with transformers library version 4.37.0+
Requires PyTorch 2.0+ for optimal performance
Flash Attention 2 requires separate installation: pip install flash-attn
BitsAndBytes quantization requires: pip install bitsandbytes

Changelog

v1.2 (Current - November 2025)

Added GGUF format files (F16, Q8_0, Q4_K_M)
Comprehensive GGUF usage documentation (llama.cpp, Ollama, LM Studio)
Detailed hardware requirements for each format
Format selection guide for different use cases
Updated total repository size to ~46 GB
Added Python llama-cpp-python examples
Enhanced deployment flexibility across CPU/GPU systems

v1.1

Updated README with accurate file information
Added abliteration details and safety warnings
Documented single-file merged format
Added processor configuration guidance
Enhanced ethical considerations section

v1.0 (Initial)

Initial abliterated model release
16.33 GB single-file safetensors format
Based on Qwen3-VL-8B-Instruct with safety layers removed

⚠️ FINAL WARNING: This is an uncensored AI model with all safety filters removed. Use responsibly, ethically, and in compliance with all applicable laws. You are solely responsible for how you use this model and any content it generates.

wangkanai/qwen3-vl-8b-instruct

Qwen3-VL-8B-Instruct (Abliterated)

Model Description

Repository Contents

Hardware Requirements

SafeTensors Format (FP16)

GGUF Formats (Multiple Options)

CPU-Only Inference (GGUF formats)

Usage Examples

Installation

Basic Image Understanding

Multi-Turn Conversation

OCR and Document Understanding

Loading with Safetensors Library Directly

GGUF Format Usage

Using with llama.cpp

Using with Ollama

Using with LM Studio

Python with llama-cpp-python

Format Selection Guide

Model Specifications

Architecture Details

Input Specifications

Generation Parameters

Supported Tasks

Performance Tips and Optimization

Memory Optimization

Inference Optimization

Generation Strategy

Abliteration Details

License

Citation

Model Card Contact

Links and Resources

Limitations and Considerations

Technical Notes

Single-File Format

Processor Configuration

Compatibility Notes

Changelog

wangkanai/
qwen3-vl-8b-instruct