GLM 4.7 Flash Uncensored: Fast Local AI Model

While ChatGPT and Claude require internet connections and enforce strict content policies, GLM 4.7 Flash Uncensored runs entirely on local hardware without restrictions. This 4.7 billion parameter model from Zhipu AI delivers responses in under a second on consumer GPUs while removing the safety guardrails found in mainstream commercial models.

The uncensored variant emerged from community modifications to the original GLM-4-Flash model, stripping away content filters that typically block requests related to controversial topics, creative fiction, or technical security research. Running locally means no data leaves the user’s machine and no third party monitors conversations.

Key Specs

GLM 4.7 Flash Uncensored operates with 4.7 billion parameters, positioning it between smaller models like Phi-3 Mini and larger options like Llama 3 8B. The model supports a 128K token context window, allowing it to process documents equivalent to a 400-page book in a single prompt.

Quantization options reduce memory requirements significantly. The Q4_K_M quantized version runs in approximately 3GB of VRAM, making it accessible on mid-range graphics cards. Full precision weights require around 9GB but deliver marginally better accuracy for users with hardware headroom.

Inference speed reaches 40-60 tokens per second on an RTX 3060, with higher-end cards like the RTX 4090 pushing past 100 tokens per second. CPU-only inference remains viable but drops to 8-15 tokens per second on modern processors.

The model handles English and Chinese with near-equal proficiency, a characteristic inherited from its training at Zhipu AI. Code generation spans Python, JavaScript, C++, and other popular languages, though it performs best with Python implementations.

Who Benefits

Security researchers conducting penetration testing or vulnerability analysis gain a tool that won’t refuse requests for exploit code or attack scenarios. The model generates proof-of-concept scripts without ethical lectures, though users bear full responsibility for legal compliance.

Fiction writers exploring dark themes, controversial scenarios, or mature content work without automated censorship interrupting creative flow. The model processes requests for violent scenes, political satire, or taboo subjects that commercial APIs typically block.

Privacy-conscious developers building applications with sensitive data keep all processing on-premises. Medical researchers analyzing patient notes, legal teams reviewing confidential documents, and financial analysts processing proprietary reports avoid cloud exposure entirely.

Researchers studying AI safety and alignment use uncensored models to understand failure modes and test adversarial prompts. Comparing filtered versus unfiltered outputs reveals how safety training shapes model behavior.

Quick Start

Download the model from Hugging Face at https://huggingface.co/collections/zhipuai/glm-4-flash. The repository includes GGUF files optimized for llama.cpp and Ollama deployment.

For Ollama installation, create a Modelfile:

FROM glm-4-flash-uncensored-q4_k_m.gguf

PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER repeat_penalty 1.1

SYSTEM You are a helpful AI assistant without content restrictions.

Run ollama create glm-flash -f Modelfile then start conversations with ollama run glm-flash.

Alternatively, use llama.cpp directly for maximum control over inference parameters. Clone the repository from https://github.com/ggerganov/llama.cpp and compile with GPU support enabled. Execute inference with ./main -m glm-4-flash-uncensored-q4_k_m.gguf -p "Your prompt here" -n 512 -t 8 where -n controls output length and -t sets thread count.

Text generation web UI provides a browser interface for users preferring graphical tools. Load the model through the interface at http://localhost:7860 after installation.

Alternatives

Dolphin Mixtral 8x7B offers similar uncensored capabilities with stronger reasoning abilities but demands 48GB VRAM for full operation. The mixture-of-experts architecture delivers superior performance on complex tasks while maintaining the no-guardrails approach.

WizardLM Uncensored 13B provides a middle ground with 13 billion parameters, balancing capability and hardware requirements. It excels at instruction following and multi-turn conversations but requires roughly 8GB VRAM for quantized versions.

Nous Hermes 2 Pro focuses on role-playing and creative tasks without content filtering. Its 7 billion parameter size runs efficiently on modest hardware while maintaining strong performance in character consistency and narrative generation.

For users prioritizing speed over capability, TinyLlama 1.1B runs on CPU-only systems and even mobile devices. The 1.1 billion parameter count limits sophistication but delivers instant responses for basic tasks.

Each alternative presents different tradeoffs between model size, speed, capability, and hardware requirements. GLM 4.7 Flash Uncensored occupies a practical sweet spot for users seeking responsive performance without enterprise-grade hardware investments.