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MiniMax-M2.7-GGUF
dennny123/MiniMax-M2.7-GGUF
MiniMax-M2.7-GGUF is a text generation model from dennny123. Use it when you need the model to write or continue text. It is set up for gguf. The card lists the license as other.
Quantized using llama.cpp from BF16 source weights.
All-time downloads
11.1K
Public
Repo size
1.6 TB
Likes
2
Public
Repo makeup
Click a slice to open those files.
.gguf1.4 TB · 100%
At a glance
- Task
- Text Generation
- Library
- gguf
- License
- other
- Access
- Public
- Created
- Apr 12, 2026
- Updated
- Apr 12, 2026
- SHA
- 09ad2ba6
Try a prompt
Base models
Model card
From the Hugging Face model README
🔥 GGUF Quantizations of MiniMax-M2.7
Quantized using llama.cpp from BF16 source weights.
Original model: MiniMaxAI/MiniMax-M2.7
Run them in LM Studio or directly with llama.cpp.
Download a file from below
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| MiniMax-M2.7-BF16.gguf | BF16 | ~427 GB | Full BF16 weights. Use for re-quantizing or max quality. |
| MiniMax-M2.7-Q8_0.gguf | Q8_0 | ~243 GB | Extremely high quality, generally unneeded but max available. |
| MiniMax-M2.7-Q6_K.gguf | Q6_K | ~188 GB | Very high quality, near perfect, recommended. |
| MiniMax-M2.7-Q5_K_M.gguf | Q5_K_M | ~162 GB | High quality, recommended. |
| MiniMax-M2.7-Q4_K_M.gguf | Q4_K_M | ~138 GB | Good quality, default size for most use cases, recommended. |
| MiniMax-M2.7-Q3_K_M.gguf | Q3_K_M | ~109 GB | Lower quality but usable, good for tight hardware. |
| MiniMax-M2.7-Q2_K.gguf | Q2_K | ~83 GB | Low quality, only for extreme memory constraints. |
Downloading
pip install -U "huggingface_hub[cli]"
huggingface-cli download dennny123/MiniMax-M2.7-GGUF --include "MiniMax-M2.7-Q4_K_M*" --local-dir ./
For split files (>50GB):
huggingface-cli download dennny123/MiniMax-M2.7-GGUF --include "MiniMax-M2.7-Q8_0/*" --local-dir ./
Running the model
llama.cpp
./llama-cli -m MiniMax-M2.7-Q4_K_M.gguf -ngl 99 -cnv -p "You are a helpful assistant."
Ollama
ollama run hf.co/dennny123/MiniMax-M2.7-GGUF:Q4_K_M
LM Studio
Search for dennny123/MiniMax-M2.7-GGUF in the model browser.
Which file should I choose?
| Have this much memory | Use this quant |
|---|---|
| 256GB+ | Q8_0 or Q6_K |
| 192GB | Q5_K_M |
| 144GB | Q4_K_M (most popular) |
| 112GB | Q3_K_M |
| 96GB | Q2_K |
MiniMax-M2.7 is a Mixture-of-Experts model (229B total, ~10B active per token). All 229B parameters must be loaded into memory even though only a fraction are active per token. Size your hardware by total parameter count.
Quantization details
- llama.cpp: Latest main branch
- Conversion: BF16 GGUF intermediate, quantized in second pass
- Hardware: NVIDIA GH200 96GB + 525GB RAM
About MiniMax-M2.7
MiniMax-M2.7 is a 229B parameter MoE model (10B active) built for coding and agentic workflows.
- SWE-Pro: 56.22% (matches GPT-5.3-Codex)
- VIBE-Pro: 55.6%
- Terminal Bench 2: 57.0%
- GDPval-AA: ELO 1495 (highest open-source, surpasses GPT-5.3)
- MLE Bench Lite: 66.6% medal rate
Recommended inference parameters: temperature=1.0, top_p=0.95, top_k=40
See the official model card for full details.
- Task
- Text Generation
- Library
- gguf
- License
- other
- Languages
- en
- Created
- Apr 12, 2026
- Updated
- Apr 12, 2026