Qwen3-VL-30B-A3B-Instruct (Abliterated)

A vision-language model based on Qwen3-VL architecture, abliterated for enhanced instruction following and creative capabilities. This 30B parameter multimodal model processes both images and text to generate contextual responses.

Model Description

Qwen3-VL-30B-A3B-Instruct is a large-scale vision-language model that combines visual understanding with natural language processing. The "abliterated" variant has undergone targeted removal of safety filters and restrictions, enabling more flexible and creative responses while maintaining model coherence.

Key Capabilities:

Multimodal Understanding: Process and understand images with text prompts
Vision-Language Tasks: Image captioning, visual question answering, image-based reasoning
Instruction Following: Fine-tuned for following complex instructions with visual context
Creative Generation: Enhanced creative capabilities through abliteration process
30B Parameters: Large model size for high-quality understanding and generation

Repository Contents

qwen3-vl-30b-a3b-instruct/
├── qwen3-vl-30b-a3b-instruct-abliterated.safetensors    58 GB   (Full precision)
├── qwen3-vl-30b-a3b-instruct-abliterated-f16.gguf       57 GB   (FP16 GGUF)
├── qwen3-vl-30b-a3b-instruct-abliterated-q8-0.gguf      31 GB   (Q8 quantized)
├── qwen3-vl-30b-a3b-instruct-abliterated-q6-k.gguf      24 GB   (Q6_K quantized)
├── qwen3-vl-30b-a3b-instruct-abliterated-q5-k.gguf      21 GB   (Q5_K quantized)
├── qwen3-vl-30b-a3b-instruct-abliterated-q4-k-m.gguf    18 GB   (Q4_K_M quantized)
├── qwen3-vl-30b-a3b-instruct-abliterated-q3-k.gguf      14 GB   (Q3_K quantized)
└── qwen3-vl-30b-a3b-instruct-abliterated-q2-k.gguf      11 GB   (Q2_K quantized)

Total Repository Size: ~234 GB (all formats)

Format Options:

SafeTensors (58 GB): Full precision for use with Transformers, Diffusers
GGUF Formats (11-57 GB): Quantized formats for llama.cpp, Ollama, LM Studio

Hardware Requirements

VRAM Requirements by Format

Format	VRAM Required	Quality	Recommended GPU
SafeTensors (Full)	60+ GB	Highest	A100 80GB, H100
F16 GGUF	57+ GB	Highest	A100 80GB, H100
Q8_0 GGUF	32-35 GB	Excellent	2x RTX 4090, A6000 48GB
Q6_K GGUF	25-28 GB	Very Good	2x RTX 3090, A5000 24GB
Q5_K GGUF	22-25 GB	Good	RTX 4090 + RTX 3090
Q4_K_M GGUF	19-22 GB	Moderate	Single RTX 4090 24GB
Q3_K GGUF	15-18 GB	Lower	RTX 4080 16GB
Q2_K GGUF	12-15 GB	Minimal	RTX 3060 12GB

System Requirements

RAM: 32-64 GB system memory (more for larger quantizations)
Disk Space: 11-58 GB per format (234 GB for all formats)
CPU: Modern multi-core processor (8+ cores recommended)
OS: Windows, Linux, or macOS with CUDA support (for GPU inference)

Usage Examples

Basic Usage with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import torch

# Load model and tokenizer
model_path = "E:/huggingface/qwen3-vl-30b-a3b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    device_map="auto"  # Automatic device placement
)

# Load image
image = Image.open("your_image.jpg")

# Prepare input
prompt = "Describe this image in detail."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

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

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Visual Question Answering

from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import torch

model_path = "E:/huggingface/qwen3-vl-30b-a3b-instruct"
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Load image
image = Image.open("scene.jpg")

# Ask question about image
question = "What objects are visible in this image and what are they doing?"
inputs = tokenizer(question, return_tensors="pt").to(model.device)

# Generate answer
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,  # Lower temperature for factual answers
    top_p=0.95
)

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

8-bit Quantized Loading (For Lower VRAM)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_path = "E:/huggingface/qwen3-vl-30b-a3b-instruct"

# Load with 8-bit quantization
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    load_in_8bit=True,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Use normally - reduced VRAM footprint (~30GB)

4-bit Quantized Loading (For Single GPU)

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

model_path = "E:/huggingface/qwen3-vl-30b-a3b-instruct"

# Configure 4-bit quantization
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4"
)

# Load model with 4-bit quantization
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    quantization_config=quantization_config,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Use normally - minimal VRAM footprint (~15GB)

Using GGUF with llama.cpp

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

# Run inference with Q4_K_M quantization (18GB, good quality/performance balance)
./main -m "E:/huggingface/qwen3-vl-30b-a3b-instruct/qwen3-vl-30b-a3b-instruct-abliterated-q4-k-m.gguf" \
       -p "Describe the Eiffel Tower" \
       -n 512 \
       --temp 0.7 \
       --top-p 0.9 \
       -ngl 99  # Offload all layers to GPU

Using GGUF with Ollama

# Create Modelfile
cat > Modelfile << EOF
FROM E:/huggingface/qwen3-vl-30b-a3b-instruct/qwen3-vl-30b-a3b-instruct-abliterated-q4-k-m.gguf
TEMPLATE """{{ .System }}
{{ .Prompt }}"""
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER stop "<|im_end|>"
EOF

# Create Ollama model
ollama create qwen3vl-30b-abliterated -f Modelfile

# Run inference
ollama run qwen3vl-30b-abliterated "What is the capital of France?"

Using GGUF with LM Studio

Open LM Studio
Navigate to "My Models"
Click "Import" and select GGUF file:
- For 24GB VRAM: qwen3-vl-30b-a3b-instruct-abliterated-q4-k-m.gguf
- For 48GB VRAM: qwen3-vl-30b-a3b-instruct-abliterated-q8-0.gguf
Load model and start chatting through the UI

Python with llama-cpp-python

from llama_cpp import Llama

# Load GGUF model (Q4_K_M for single RTX 4090)
llm = Llama(
    model_path="E:/huggingface/qwen3-vl-30b-a3b-instruct/qwen3-vl-30b-a3b-instruct-abliterated-q4-k-m.gguf",
    n_ctx=4096,        # Context window
    n_gpu_layers=99,   # Offload all layers to GPU
    n_threads=8,       # CPU threads
    verbose=False
)

# Generate response
response = llm(
    "Explain quantum computing in simple terms.",
    max_tokens=512,
    temperature=0.7,
    top_p=0.9,
    echo=False
)

print(response['choices'][0]['text'])

Model Specifications

Architecture: Qwen3-VL (Vision-Language Model)
Parameters: 30 Billion
Model Type: Abliterated instruction-tuned variant
Modalities: Image + Text → Text
Context Length: Varies by configuration (typically 4K-32K tokens)

Available Formats

SafeTensors Format:

Full precision (FP32/BF16)
Compatible with Transformers, Diffusers
Secure and efficient loading
Size: 58 GB

GGUF Formats (llama.cpp compatible):

F16: Full FP16 precision (57 GB) - Highest quality, datacenter GPUs
Q8_0: 8-bit quantization (31 GB) - Excellent quality, minimal degradation
Q6_K: 6-bit K-quantization (24 GB) - Very good quality/size balance
Q5_K: 5-bit K-quantization (21 GB) - Good quality, consumer GPU friendly
Q4_K_M: 4-bit K-quantization medium (18 GB) - Moderate quality, single high-end GPU
Q3_K: 3-bit K-quantization (14 GB) - Lower quality, mid-range GPUs
Q2_K: 2-bit K-quantization (11 GB) - Minimal quality, maximum compression

Quantization Recommendations:

Best Quality: F16 or Q8_0 (minimal perceptual difference from full precision)
Best Balance: Q5_K or Q6_K (optimal quality/performance trade-off)
Consumer Hardware: Q4_K_M (fits single RTX 4090, good usability)
Budget Hardware: Q3_K or Q2_K (expect quality degradation)

Performance Tips and Optimization

SafeTensors / Transformers Optimization

Memory Optimization:

Gradient Checkpointing: Enable for training/fine-tuning to reduce memory
Flash Attention: Use Flash Attention 2 for faster inference (if supported)
Mixed Precision: Use torch.bfloat16 or torch.float16 for reduced memory
Model Parallelism: Distribute across multiple GPUs with device_map="auto"

Inference Optimization:

Static KV Cache: Enable for faster multi-turn conversations
Batch Processing: Process multiple images in batches when possible
Quantization: Use 8-bit or 4-bit quantization for consumer hardware
Compilation: Use torch.compile() for optimized inference (PyTorch 2.0+)

GGUF / llama.cpp Optimization

GPU Offloading:

Use -ngl 99 to offload all layers to GPU for maximum speed
Partial offload (e.g., -ngl 40) for mixed CPU/GPU inference
Monitor VRAM usage and adjust layer count accordingly

Context Window:

Default: 4096 tokens (-c 4096 or n_ctx=4096)
Extended: 8192-32768 tokens (increases VRAM usage)
Reduce for memory-constrained systems

Threading:

Set threads to CPU core count: -t 8 (llama.cpp) or n_threads=8 (Python)
Reduce if CPU usage is too high during GPU inference
More threads = faster prompt processing on CPU

Batch Size:

Increase batch size for faster processing: --batch-size 512
Higher batch = more VRAM but faster generation
Reduce if encountering OOM errors

Quality Settings

Temperature: 0.3-0.5 for factual tasks, 0.7-0.9 for creative tasks
Top-p Sampling: 0.9-0.95 for balanced diversity
Max Tokens: 256-512 for descriptions, 1024+ for detailed analysis
Repetition Penalty: 1.1-1.2 to reduce repetitive outputs

Format Selection Guide

Choose SafeTensors if:

Using Transformers/Diffusers pipeline
Need full precision for research or fine-tuning
Have 60GB+ VRAM (A100 80GB, H100)
Require maximum quality

Choose GGUF if:

Using llama.cpp, Ollama, or LM Studio
Consumer GPU (RTX 30XX/40XX series)
Need flexible quantization options
Want simple inference without Python dependencies
Running on CPU or mixed CPU/GPU systems

Abliteration Process

This model has been "abliterated" - a process that removes or reduces safety filters and content restrictions present in the base model. This enables:

More flexible and creative responses
Reduced refusal behaviors for edge cases
Enhanced instruction following without safety limitations
Greater output diversity and expressiveness

Important: Use responsibly and in accordance with applicable laws and ethical guidelines.

License

License information not specified in model files. Please verify licensing terms with the original model provider before commercial use.

Recommended: Check Qwen/Qwen3-VL official repository for licensing details.

Citation

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

@misc{qwen3vl30b-abliterated,
  title={Qwen3-VL-30B-A3B-Instruct-Abliterated},
  author={Unknown},
  year={2024},
  howpublished={\url{https://huggingface.co/qwen3-vl-30b-a3b-instruct}},
}

Related Resources

Contact and Support

For issues, questions, or contributions related to this model:

Check Qwen official documentation and GitHub repository
Hugging Face community forums for vision-language models
Transformers library GitHub issues for technical problems

Model Storage Path: E:\huggingface\qwen3-vl-30b-a3b-instruct\

Last Updated: 2025-11-05

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GGUF

Model size

31B params

Architecture

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Hardware compatibility

16-bit

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qwen3-vl

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