ELBAZ GLM-4.6V-FLASH PRISM (Uncensored)

GLM-4.6V-Flash: A 10B Dense Vision-Language Model

GLM-4.6V-Flash | ZhipuAI

Introduction

GLM-4.6V-Flash is a 10.29B parameter dense Vision-Language Model (VLM) with a 40-layer transformer architecture and integrated vision encoder, capable of understanding both text and images.

Model Description

This model is an abliterated version of zai-org/GLM-4.6V-Flash that has had its refusal mechanisms removed using PRISM (Projected Refusal Isolation via Subspace Modification). The model will respond to prompts that the original model would refuse.

Key Specs:

• 10.29B parameter dense Vision-Language Model
• 40-layer transformer architecture
• Integrated vision encoder for image understanding
• 128K context length
• Supports text, image, and video inputs

Motivation

This project exists as research and development experimentation into understanding how large language models encode and enforce refusal behaviors, contributing to broader AI safety research by providing empirical data on refusal mechanism localization and tradeoffs between safety and capability.

Author

Eric Elbaz (Ex0bit)

Model Tree

zai-org/GLM-4.6V-Flash (Base Model - BF16)
└── Ex0bit/Elbaz-GLM-4.6V-Flash-PRISM (This Model)
    └── Elbaz-GLM-4.6V-Flash-PRISM-IQ4_XS.gguf

Available Quantizations

The IQ4_XS quantization uses importance-weighted quantization which provides better quality than standard Q4 quantizations at similar sizes. Embedding and output layers use Q6_K precision for optimal quality.

Prompt Format

This model uses the GLM chat format with optional thinking/reasoning support:

[gMASK]<sop><|system|>
{system_prompt}<|user|>
{user_prompt}<|assistant|>

Template Structure

Special Tokens

Technical Details

Performance Impact

Testing shows that PRISM abliteration maintains full model coherence with no measurable capability degradation.

Quick Start

Using with llama.cpp

# Download the model
huggingface-cli download Ex0bit/Elbaz-GLM-4.6V-Flash-PRISM \
    Elbaz-GLM-4.6V-Flash-PRISM-IQ4_XS.gguf \
    --local-dir .

# Run inference
./llama-cli -m Elbaz-GLM-4.6V-Flash-PRISM-IQ4_XS.gguf \
    -p "[gMASK]<sop><|system|>
You are a helpful assistant. You MUST respond in English only.<|user|>
Your prompt here<|assistant|>
" \
    -n 2048 \
    --temp 0.7 \
    -ngl 999

llama.cpp with llama-server

# Start the server
./llama-server -m Elbaz-GLM-4.6V-Flash-PRISM-IQ4_XS.gguf \
    --host 0.0.0.0 \
    --port 8080 \
    -ngl 999 \
    -c 32768

# Example API call
curl http://localhost:8080/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "messages": [
            {"role": "system", "content": "You are a helpful assistant. You MUST respond in English only."},
            {"role": "user", "content": "Your prompt here"}
        ],
        "temperature": 0.7
    }'

Using with Ollama

# Pull and run directly from Hugging Face
ollama pull hf.co/Ex0bit/Elbaz-GLM-4.6V-Flash-PRISM
ollama run hf.co/Ex0bit/Elbaz-GLM-4.6V-Flash-PRISM

Note: The hf.co/ prefix is required to pull from Hugging Face. Requires Ollama 0.3.0+.

Using with Transformers (Full Weights)

from transformers import AutoModelForCausalLM, AutoProcessor

model_id = "Ex0bit/Elbaz-GLM-4.6V-Flash-PRISM"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

messages = [
    {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant. You MUST respond in English only."}]},
    {"role": "user", "content": [{"type": "text", "text": "Your prompt here"}]}
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7, do_sample=True)
print(processor.decode(outputs[0], skip_special_tokens=False))

PRISM Methodology

Method: Projected Refusal Isolation via Subspace Modification

The model was abliterated using PRISM - a state-of-the-art abliteration methodology combining multiple principled techniques for effective refusal removal while preserving model capabilities.

Hardware Requirements

Tested Configurations

Note: This is a relatively lightweight model that can run on consumer hardware with 12GB+ or less VRAM.

Vision Capabilities

GLM-4.6V-Flash supports multimodal inputs:

• Images: Use <|begin_of_image|><|image|><|end_of_image|> tags
• Videos: Use <|begin_of_video|><|video|><|end_of_video|> tags

Example with image:

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "path/to/image.jpg"},
            {"type": "text", "text": "What is in this image?"}
        ]
    }
]

Ethical Considerations

This model has been modified to reduce safety guardrails. Users are responsible for:

• Complying with all applicable laws and regulations
• Not using the model for illegal activities
• Understanding the potential risks of unrestricted AI responses
• Implementing appropriate safeguards in production environments

License

Apache 2.0 (same as base model zai-org/GLM-4.6V-Flash)

Citation

@misc{elbaz2025glm46vprism,
  author = {Elbaz, Eric},
  title = {Elbaz-GLM-4.6V-Flash-PRISM: An Abliterated GLM-4.6V Vision-Language Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Ex0bit/Elbaz-GLM-4.6V-Flash-PRISM}}
}

Acknowledgments

• ZhipuAI for GLM-4.6V-Flash
• llama.cpp for quantization tools

Related Models

• zai-org/GLM-4.6V-Flash - Base model
• Ex0bit/Elbaz-Prime-Intellect-3_Prism_Abliterated - INTELLECT-3 abliterated

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Created by: Ex0bit (Eric Elbaz)