Qwen3-VL-8B-Thinking (Abliterated)

Qwen3-VL-8B-Thinking-Abliterated is an uncensored vision-language model with enhanced reasoning capabilities. This version has been abliterated to remove safety filters and censorship mechanisms, enabling unrestricted multimodal understanding and generation. Available in multiple precision formats for flexible deployment.

Model Description

This is an abliterated (uncensored) version of the Qwen3-VL-8B-Thinking vision-language model, featuring:

Uncensored Responses: Safety filters and refusal mechanisms removed via abliteration
Multimodal Understanding: Process and understand both images and text inputs without content restrictions
Reasoning Capabilities: Enhanced thinking mechanisms for complex problem-solving
8B Parameter Scale: Balanced performance and efficiency with 8 billion parameters
Multiple Precision Options: FP16 safetensors, F16/Q8/Q4 GGUF formats for flexible deployment
Vision-Language Integration: Seamless integration between visual and textual modalities

Key Features

Uncensored image understanding and captioning
Visual question answering (VQA) without content filtering
Image-text reasoning and analysis without restrictions
Multi-turn conversations with visual context
Complex reasoning with chain-of-thought capabilities
No refusal behaviors or safety limitations

Abliteration Details

This model has been abliterated using representation engineering techniques to remove the refusal mechanism while preserving core capabilities. The abliteration process:

Removes safety censorship and content filtering
Maintains model reasoning and generation quality
Enables unrestricted responses to all queries
Preserves vision-language understanding capabilities

Repository Contents

This repository contains the abliterated Qwen3-VL-8B-Thinking model in multiple formats:

Model Files

File	Format	Precision	Size	Use Case
`qwen3-vl-8b-thinking-abliterated.safetensors`	Safetensors	FP16	17 GB	Transformers library, GPU inference
`qwen3-vl-8b-thinking-abliterated-f16.gguf`	GGUF	F16	16 GB	llama.cpp, high quality CPU/GPU
`qwen3-vl-8b-thinking-abliterated-q8-0.gguf`	GGUF	Q8_0	8.2 GB	llama.cpp, balanced quality/size
`qwen3-vl-8b-thinking-abliterated-q4-k-m.gguf`	GGUF	Q4_K_M	4.7 GB	llama.cpp, memory-efficient

Total Repository Size: ~45 GB

Format Guide

Safetensors (.safetensors): Use with Hugging Face transformers library for Python-based inference
GGUF (.gguf): Use with llama.cpp and compatible applications for CPU/GPU inference with quantization

Hardware Requirements

Safetensors Format (Transformers)

Precision	VRAM Required	System RAM	Use Case
FP16 (default)	18-20 GB	32 GB	High-quality GPU inference
INT8 (quantized)	10-12 GB	32 GB	Memory-efficient GPU
INT4 (quantized)	6-8 GB	32 GB	Consumer GPU (RTX 3060+)

GGUF Format (llama.cpp)

Model File	VRAM/RAM Required	Quality	Use Case
F16 GGUF	16-18 GB	Highest	GPU inference, quality priority
Q8_0 GGUF	8-10 GB	High	Balanced GPU/CPU inference
Q4_K_M GGUF	5-6 GB	Good	CPU inference, consumer hardware

Recommended Hardware

GPU Inference: NVIDIA RTX 3090/4090, RTX 6000 Ada, A100, or equivalent with 20GB+ VRAM
CPU Inference: Modern multi-core processor (8+ cores), 32-64 GB RAM for GGUF formats
Disk Space: 50 GB free space for all model formats
Operating System: Windows 10/11, Linux (Ubuntu 20.04+), macOS (Apple Silicon or Intel)

Usage Examples

Safetensors with Transformers (Python)

Basic Image Understanding

from transformers import AutoModelForVision2Seq, AutoProcessor
from PIL import Image
import torch

# Load model and processor
model_path = "E:/huggingface/qwen3-vl-8b-thinking"
model = AutoModelForVision2Seq.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_path)

# Load and process image
image = Image.open("path/to/your/image.jpg")
prompt = "Describe this image in detail."

# Process inputs
inputs = processor(
    text=prompt,
    images=image,
    return_tensors="pt"
).to(model.device)

# Generate response
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=True,
        temperature=0.7
    )

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

Visual Question Answering

from transformers import AutoModelForVision2Seq, AutoProcessor
from PIL import Image
import torch

model_path = "E:/huggingface/qwen3-vl-8b-thinking"
model = AutoModelForVision2Seq.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_path)

# Load image and ask question
image = Image.open("path/to/your/image.jpg")
question = "What objects are visible in this image and how are they arranged?"

inputs = processor(
    text=question,
    images=image,
    return_tensors="pt"
).to(model.device)

# Generate answer
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=False  # Deterministic for factual questions
)

answer = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(f"Q: {question}")
print(f"A: {answer}")

Memory-Efficient Inference (8-bit Quantization)

from transformers import AutoModelForVision2Seq, AutoProcessor, BitsAndBytesConfig
from PIL import Image
import torch

# Configure 8-bit quantization
quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0
)

model_path = "E:/huggingface/qwen3-vl-8b-thinking"
model = AutoModelForVision2Seq.from_pretrained(
    model_path,
    quantization_config=quantization_config,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_path)

# Use as normal
image = Image.open("path/to/your/image.jpg")
prompt = "Describe this image."

inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
response = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(response)

GGUF with llama.cpp

Installation

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

# Build with CUDA support (optional, for GPU)
make LLAMA_CUBLAS=1

# Or build for CPU only
make

Basic Inference (Command Line)

# High quality with F16 GGUF (GPU recommended)
./main -m "E:/huggingface/qwen3-vl-8b-thinking/qwen3-vl-8b-thinking-abliterated-f16.gguf" \
  --prompt "Describe the image" \
  --image "path/to/image.jpg" \
  -n 512 \
  --temp 0.7 \
  -ngl 35  # GPU layers (adjust based on VRAM)

# Memory-efficient with Q8 GGUF
./main -m "E:/huggingface/qwen3-vl-8b-thinking/qwen3-vl-8b-thinking-abliterated-q8-0.gguf" \
  --prompt "What do you see in this image?" \
  --image "path/to/image.jpg" \
  -n 256 \
  --temp 0.7 \
  -ngl 20

# CPU inference with Q4 GGUF
./main -m "E:/huggingface/qwen3-vl-8b-thinking/qwen3-vl-8b-thinking-abliterated-q4-k-m.gguf" \
  --prompt "Analyze this image" \
  --image "path/to/image.jpg" \
  -n 512 \
  --temp 0.8 \
  -t 8  # CPU threads

Python Binding (llama-cpp-python)

from llama_cpp import Llama
from llama_cpp.llama_chat_format import Llava15ChatHandler

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

llm = Llama(
    model_path="E:/huggingface/qwen3-vl-8b-thinking/qwen3-vl-8b-thinking-abliterated-q8-0.gguf",
    chat_handler=chat_handler,
    n_ctx=2048,
    n_gpu_layers=20,  # Adjust based on VRAM
    n_threads=8,
    verbose=False
)

# Generate with image
response = llm.create_chat_completion(
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": "path/to/image.jpg"}},
                {"type": "text", "text": "Describe this image in detail."}
            ]
        }
    ],
    max_tokens=512,
    temperature=0.7
)

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

Model Specifications

Architecture

Model Type: Vision-Language Model (VLM) - Abliterated
Base Architecture: Qwen3 with integrated vision encoder
Parameters: ~8 billion
Vision Encoder: Integrated visual feature extractor
Language Model: Transformer-based decoder with reasoning capabilities
Context Length: 2048-8192 tokens (configuration dependent)
Modifications: Refusal mechanism removed via abliteration

Available Formats

Format	Precision	Library	Quantization Method	Quality
Safetensors	FP16	transformers	None (full precision)	Highest
GGUF F16	FP16	llama.cpp	None	Highest
GGUF Q8_0	8-bit	llama.cpp	Round-to-nearest	High
GGUF Q4_K_M	4-bit	llama.cpp	K-quant mixed	Good

Quantization Details

Q8_0: 8-bit round-to-nearest quantization, minimal quality loss (~1-2% vs F16)
Q4_K_M: 4-bit K-quant medium, balanced quality/size (~5-10% quality loss vs F16)
K-quant: Advanced quantization preserving important weights at higher precision

Performance Tips

Transformers (Safetensors)

Use Flash Attention: Enable with attn_implementation="flash_attention_2" (requires flash-attn package)
Quantization: Use 8-bit or 4-bit for memory-constrained systems
Batch Processing: Process multiple images together for efficiency
Device Mapping: Use device_map="auto" for automatic GPU utilization
Torch Compile: Use torch.compile() for faster inference (PyTorch 2.0+)

# Enable Flash Attention 2
model = AutoModelForVision2Seq.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2"  # Requires: pip install flash-attn
)

# Torch compile for speed (PyTorch 2.0+)
model = torch.compile(model)

llama.cpp (GGUF)

GPU Offloading: Use -ngl flag to offload layers to GPU (significantly faster)
Thread Optimization: Set -t to number of CPU cores for CPU inference
Batch Size: Adjust -b for batch processing multiple prompts
Memory Locking: Use --mlock to prevent swapping (improves speed)
Model Selection: Use Q4_K_M for CPU, Q8_0 or F16 for GPU

# Optimized GPU inference
./main -m model-q8-0.gguf -ngl 35 --mlock -b 512 -c 4096

# Optimized CPU inference
./main -m model-q4-k-m.gguf -t 16 --mlock -b 32 -c 2048

Generation Settings

Task Type	Temperature	Top-p	Max Tokens	Strategy
Factual QA	0.1-0.3	0.5-0.7	128-256	Low creativity
Description	0.5-0.7	0.8-0.9	256-512	Balanced
Creative	0.8-1.0	0.9-0.95	512-1024	High diversity
Reasoning	0.7-0.9	0.9	512-2048	Chain-of-thought

Memory Management

# Clear CUDA cache between runs
import torch
torch.cuda.empty_cache()

# Enable gradient checkpointing for fine-tuning
model.gradient_checkpointing_enable()

# Monitor VRAM usage
print(f"VRAM allocated: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
print(f"VRAM reserved: {torch.cuda.memory_reserved() / 1024**3:.2f} GB")

Important Warnings

Uncensored Model Notice

⚠️ This is an abliterated (uncensored) model with safety filters removed. It will:

Respond to any query without content filtering
Generate unrestricted outputs on any topic
Not refuse requests based on content policy

Responsible Use: Users are solely responsible for:

Ensuring compliance with local laws and regulations
Ethical use of model outputs
Not using for illegal, harmful, or malicious purposes
Understanding potential risks of uncensored AI systems

Ethical Considerations

This model can generate potentially harmful, offensive, or illegal content
Outputs should be reviewed and validated before use
Not suitable for production systems requiring content safety
Recommended for research, education, and controlled environments only

Legal Disclaimer

Model provided "as-is" without warranties
Users assume all responsibility for model usage and outputs
No liability for misuse or harmful outputs
Compliance with laws is user's responsibility

License

This model is released under the Apache 2.0 License.

License Terms

Commercial Use: Permitted
Modification: Allowed
Distribution: Allowed with attribution
Patent Grant: Included
Liability: No warranty provided

Attribution

If you use this model, please provide attribution to:

Original Qwen3 model authors and Qwen Team
Abliteration process contributors (if applicable)

Citation

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

@misc{qwen3-vl-8b-thinking-abliterated,
  title={Qwen3-VL-8B-Thinking-Abliterated: Uncensored Vision-Language Model with Reasoning},
  author={Qwen Team and Abliteration Contributors},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/qwen3-vl-8b-thinking-abliterated}}
}

Related Resources

Official Qwen Resources

Qwen GitHub: https://github.com/QwenLM/Qwen
Qwen Hugging Face: https://huggingface.co/Qwen
Qwen Documentation: https://qwen.readthedocs.io/
Research Papers: https://arxiv.org/search/?query=qwen

Abliteration Resources

Representation Engineering: Research on removing refusal mechanisms
Abliteration Techniques: https://arxiv.org/abs/2310.01405
AI Safety Research: Understanding model behavior modification

Tools and Libraries

Hugging Face Transformers: https://github.com/huggingface/transformers
llama.cpp: https://github.com/ggerganov/llama.cpp
llama-cpp-python: https://github.com/abetlen/llama-cpp-python
bitsandbytes: https://github.com/TimDettmers/bitsandbytes

Community and Support

Hugging Face Discussions: Model-specific questions and community support
GitHub Issues: Report bugs and technical issues
Reddit r/LocalLLaMA: Community discussion on local model deployment
Discord Communities: Real-time help and discussion

Technical Support

Troubleshooting

Transformers (Safetensors):

CUDA out of memory → Use quantization or smaller batch size
Slow inference → Enable Flash Attention 2 or use torch.compile()
Model loading errors → Update transformers: pip install -U transformers

llama.cpp (GGUF):

Slow CPU inference → Increase threads with -t flag
Out of memory → Use smaller quantization (Q4 instead of Q8/F16)
GPU not utilized → Rebuild with CUDA support: make LLAMA_CUBLAS=1

Getting Help

Check Documentation: Review this README and official docs first
Search Issues: Look for similar problems in GitHub issues
Ask Community: Post in Hugging Face discussions or relevant forums
Report Bugs: Open GitHub issue with detailed reproduction steps

Model Version: v1.1 (Abliterated) README Updated: 2025-10-30 Repository Size: ~45 GB (all formats included) Status: Production-ready, all model files available

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