Instructions to use kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF:Q8_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF:Q8_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF:Q8_0
Use Docker
docker model run hf.co/kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF with Ollama:
ollama run hf.co/kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF with Docker Model Runner:
docker model run hf.co/kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF:Q8_0
- Lemonade
How to use kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF:Q8_0
Run and chat with the model
lemonade run user.MiMo-V2.6-Pro-RL-MXFP4-GGUF-Q8_0
List all available models
lemonade list
- Atomic Chat
MiMo-V2.6 Pro RL GGUF for DwarfStar
GGUF files for the MiMo-V2.6 Pro RL port in DwarfStar (DS4),
branch kernelpool/ds4:mimo-v26
(pull request: TODO link). Metal only. The model runs over tensor parallelism
between two 512 GB Macs.
Files
The main GGUF is above the Hugging Face single-file limit, so it is stored in two parts and joined after download, the same way the official DwarfStar Q4 release is.
| File | Bytes | SHA-256 |
|---|---|---|
MiMo-V2.6-Pro-RL-MXFP4.gguf.part1 |
480,000,000,000 | 2c53578328dedbb3462f01422fa0548eccd5567ff8e2ab7254aebf74d0c3aea9 |
MiMo-V2.6-Pro-RL-MXFP4.gguf.part2 |
77,146,510,560 | 601b0ad28e8e36bf88adabb97e4994ffc9da281ec26488671350645850ed969e |
MiMo-V2.6-Pro-RL-MXFP4.gguf (joined) |
557,146,510,560 | dac336f68cecf88b8ca04ab4ea2c75b831752e0323c7152a758082826f60bf21 |
MiMo-V2.6-Pro-RL-DFlash-Q8_0.gguf |
2,941,587,648 | 3aec4c9819dacad46e5bca5ccd55b92e426e807616783f8140914854e76e4e91 |
The main file uses the mimo2 layout: the checkpoint's own MXFP4 experts
repacked without requantization, Q8_0 attention, dense and output weights,
BF16 embeddings, and the three MTP blocks. The DFlash sidecar uses the
dflash layout plus the mask embedding and value scale DS4 needs.
hf download kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF --local-dir gguf
cd gguf
cat MiMo-V2.6-Pro-RL-MXFP4.gguf.part2 >> MiMo-V2.6-Pro-RL-MXFP4.gguf.part1
mv MiMo-V2.6-Pro-RL-MXFP4.gguf.part1 MiMo-V2.6-Pro-RL-MXFP4.gguf
rm MiMo-V2.6-Pro-RL-MXFP4.gguf.part2
openssl dgst -sha256 MiMo-V2.6-Pro-RL-MXFP4.gguf # dac336f6...
Appending the second part to the first keeps the extra disk space to the size of the second part.
Run
Both Macs need the joined file locally. Each rank keeps half of the attention heads, half of the routed experts and half of the vocabulary head, about 270 GiB resident, so each Mac needs 512 GB. The link setup is in docs/DISTRIBUTED.md. Start the worker first:
git clone -b mimo-v26 https://github.com/kernelpool/ds4 && cd ds4 && make
# Machine B (worker)
./ds4 -m gguf/MiMo-V2.6-Pro-RL-MXFP4.gguf --ctx 8192 --mtp \
--tensor-parallel --role worker --coordinator 192.168.0.1 9911 --transport rdma
# Machine A (coordinator)
./ds4-server -m gguf/MiMo-V2.6-Pro-RL-MXFP4.gguf --ctx 8192 --mtp \
--tensor-parallel --role coordinator --listen 192.168.0.1 9911 --transport rdma
--mtp drafts with the MTP blocks inside the main file; both ranks run the
draft and verify it together, so pass it to both or to neither. Greedy output
follows plain decoding.
The server exposes mimo-v2.6-pro, mimo-v2.6-pro-chat (thinking off) and
mimo-v2.6-pro-reasoner (thinking on).
DS4 does not run the DFlash sidecar or vision under tensor parallelism yet, so the sidecar is not used for now and no vision encoder is included. See docs/MIMO_V26.md.
Conversion
Written by gguf-tools/mimo26_quantize.py from XiaomiMiMo/MiMo-V2.6-Pro-RL
at revision 54b10491b1811c76aa9681a9d0ff872396a4064c. The converter
de-interleaves the tensor-parallel chunks of the fused QKV projection and
keeps the released MXFP4 expert blocks bit for bit; the source revision is
recorded in the GGUF metadata.
Quality
Scored over two-Mac tensor parallelism on 100 official continuations from the
Xiaomi platform with the fixture in
gguf-tools/quality-testing/mimo-v2.6-pro-20260923; the reference scores are
in the repository's QA_BEFORE_RELEASES.md.
The original checkpoint is released by Xiaomi under the MIT license, which applies to these files as well.
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