MYTHOS-26B-A4B — PRISM Dynamic Quantization (GGUF)
Gemma 4 26B-A4B MoE PRISM-PRO-Dynamic-Quant
- PRISM-PRO: Production model with full over-refusal and bias mechanisms completely removed using State of the Art PRISM pipeline.
- DQ: Per-tensor-class mixed-precision allocation derived entirely from weight structure sensitivity analysis — not closed-gated datasets.
Created by Ex0bit
💡 Support My Research & Development efforts. Members Receive access to the latest PRISM-PRO Model drops on Day-0
Model Details
| Property | Value |
|---|---|
| Base Model | google/gemma-4-26B-A4B-it |
| Architecture | Gemma 4 MoE (128 experts, top-8 routing) |
| Parameters | 26B total / 4B active per token |
| Quantization | PRISM-PRO-DYNAMIC-QUANT |
| Achieved BPW | 5.73 |
| File Size | ~17 GB (language) + ~1.2 GB (vision projector) |
| Context Length | 262,144 tokens |
| Modalities | Text, Image, Video |
| Creator | Ex0bit |
Supported Modalities
- Text: Full instruction-following and chat
- Image: Vision understanding via SigLIP encoder (280 soft tokens per image)
- Video: Gemma4VideoProcessor (32 frames, pooled)
Note: This 26B MoE variant does not include audio support. For audio, see the 31B dense variant.
Files
| File | Size | Purpose |
|---|---|---|
mythos-26b-a4b-prism-pro-dq.gguf |
17 GB | Language model (quantized) |
mmproj-mythos-26b-a4b-prism-pro.gguf |
1.2 GB | Vision projector (F16) |
Both files are required for multimodal inference. For text-only use, only the language model file is needed.
PRISM-DQ Quantization
This model uses PRISM-PRO Dynamic Quantization — a per-tensor-class mixed-precision allocation that assigns different quantization types to different tensor classes based on weight structure sensitivity.
Unlike uniform quantization (Q4_K_M, Q5_K_M), PRISM-DQ analyzes each tensor class's sensitivity and allocates precision where it matters most. Attention projections receive higher precision than FFN layers, with block-level overrides that protect critical layers.
The result: BF16-equivalent quality at 5.73 bits-per-weight — a 64% size reduction with zero measurable quality loss.
Usage
llama.cpp (multimodal with vision)
llama-mtmd-cli \
--model mythos-26b-a4b-prism-pro-dq.gguf \
--mmproj mmproj-mythos-26b-a4b-prism-pro.gguf \
--image path/to/image.jpg \
--prompt "Describe this image." \
-ngl 99
llama.cpp (text-only server)
llama-server \
--model mythos-26b-a4b-prism-pro-dq.gguf \
--port 8080 -ngl 99
LM Studio
Download both mythos-26b-a4b-prism-pro-dq.gguf and mmproj-mythos-26b-a4b-prism-pro.gguf. LM Studio will automatically detect the vision projector for multimodal chat.
Refusal & Bias Removal
This model has been treated to remove bias, over-refusals and propaganda from the base google/gemma-4-26B-A4B-it using the State of The Art PRISM pipeline.
License
Apache 2.0 (inherited from google/gemma-4-26B-A4B-it)
Credits
- Creator: Ex0bit
- Base model: Google DeepMind
- Quantization engine: PRISM-DQ by Ex0bit