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MiniMax-M2.5-GGUF
marksverdhei/MiniMax-M2.5-GGUF
MiniMax-M2.5-GGUF is a machine learning model from marksverdhei. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
GGUF quantizations of MiniMaxAI/MiniMax-M2.5, created with llama.cpp.
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At a glance
- License
- other
- Access
- Public
- Created
- Feb 13, 2026
- Updated
- Feb 13, 2026
- SHA
- 5ba53004
Base models
Model card
From the Hugging Face model README
MiniMax-M2.5 GGUF
GGUF quantizations of MiniMaxAI/MiniMax-M2.5, created with llama.cpp.
Model Details
| Property | Value |
|---|---|
| Base model | MiniMaxAI/MiniMax-M2.5 |
| Architecture | Mixture of Experts (MoE) |
| Total parameters | 230B |
| Active parameters | 10B per token |
| Layers | 62 |
| Total experts | 256 |
| Active experts per token | 8 |
| Source precision | FP8 (float8_e4m3fn) |
Available Quantizations
| Quantization | Size | Description |
|---|---|---|
| Q8_0 | 227 GB | 8-bit quantization, highest quality |
| Q4_K_M | 129 GB | 4-bit K-quant (medium), good balance of quality and size |
| IQ3_S | 92 GB | 3-bit importance quantization (small), compact |
| Q2_K | 78 GB | 2-bit K-quant, smallest size |
Usage
These GGUFs can be used with llama.cpp and compatible frontends.
# Example with llama-cli
llama-cli -m MiniMax-M2.5-Q4_K_M.gguf -p "Hello" -n 128
Notes
- The source model uses FP8 (
float8_e4m3fn) precision, so Q8_0 is effectively lossless relative to the source weights. - This is a large MoE model. Even the smallest quant (Q2_K) requires ~78GB due to the number of experts.
- Quantized from the official MiniMaxAI/MiniMax-M2.5 weights.
- License
- other
- Created
- Feb 13, 2026
- Updated
- Feb 13, 2026