GLM-4.6V-Flash MXFP4 GGUF

Quantized GGUF version of zai-org/GLM-4.6V-Flash by Z.ai (Zhipu AI), converted to MXFP4 (4-bit Microscaling FP4) format.

Model Details

Base model: zai-org/GLM-4.6V-Flash — 9B parameter vision-language model by Z.ai with 40 transformer layers, 4096 hidden dim, 32 attention heads (8 KV heads), SwiGLU activation. Paper: 2507.01006.
Vision encoder: 24-layer ViT (1536 hidden dim, 1536/4096 attention dim, 13696 intermediate FFN)
Context length: 128K tokens
Quantization: MXFP4 — OCP Microscaling FP4 format (E2M1 data values with E8M0 per-block scales, 4.41 BPW, 4.82 GB)
Thinking: Enabled by default (native <think>/</think> tokens, opt-out via enable_thinking=false)

Files

File	Size	Description
`glm-4.6v-flash-mxfp4.gguf`	4.82 GB	Quantized text model (523 tensors, 4.41 BPW)
`mmproj-glm-4.6v-flash-f16.gguf`	1.66 GB	Vision encoder projector (182 tensors, F16)

Usage

LM Studio

Load both files — the text GGUF as the main model and the mmproj as the vision encoder. Supports multimodal inputs (images + text).

llama.cpp

./llama-llava-cli \
  -m glm-4.6v-flash-mxfp4.gguf \
  --mmproj mmproj-glm-4.6v-flash-f16.gguf \
  -p "Describe this image in detail." \
  --image path/to/image.jpg

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(
    model_path="glm-4.6v-flash-mxfp4.gguf",
    mmproj="mmproj-glm-4.6v-flash-f16.gguf",
    n_ctx=32768
)

output = llm.create_chat_completion(
    messages=[{
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": "image.jpg"}},
            {"type": "text", "text": "What's in this image?"}
        ]
    }]
)
print(output["choices"][0]["message"]["content"])

Quantization Details

Source: zai-org/GLM-4.6V-Flash → F16 GGUF → llama-quantize.exe MXFP4
Block size: 32 elements; E8M0 shared scale (1 scale per 32-element block)
Output tensor: Q6_K (higher precision for the final projection)
Format: OCP MXFP4 specification (E2M1 data values, E8M0 per-block scaling)
Architecture: glm4 with 523 tensors (40 transformer layers, vision embedder)

Hardware Compatibility

MXFP4 is supported on NVIDIA Blackwell (RTX 50 series) via native FP4 MMA instructions
Falls back to software dequantization on other GPU architectures and CPU
Cross-vendor compatible format per OCP Microscaling specification

jamiefutch/GLM-4.6V-Flash-MXFP4-GGUF