Image-Text-to-Text
GGUF
English
Chinese
mixed-quant
mimo_v2
multimodal
audio
video-understanding
imatrix
conversational
Instructions to use Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-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 Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-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 Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-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 Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-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 Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0
Use Docker
docker model run hf.co/Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0
- LM Studio
- Jan
- vLLM
How to use Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0
- Ollama
How to use Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF with Ollama:
ollama run hf.co/Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0
- Unsloth Desktop
- Pi
How to use Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF with Docker Model Runner:
docker model run hf.co/Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0
- Lemonade
How to use Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0
Run and chat with the model
lemonade run user.MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF:Q8_0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download benchmarks/2026-09-30-mimo2-mopd-spark/final-3.csv from Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 1.74 kB
-
https://huggingface.co/Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF/resolve/main/benchmarks/2026-09-30-mimo2-mopd-spark/final-3.csv
- Command line
-
hf download hf://Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF/benchmarks/2026-09-30-mimo2-mopd-spark/final-3.csv
-
curl -L -o final-3.csv https://huggingface.co/Baekpica/MiMo-V2.6-Flash-MOPD-Mixed-Quant-GGUF/resolve/main/benchmarks/2026-09-30-mimo2-mopd-spark/final-3.csv
1.74 kB
| ctx_tokens,prefill_tokens,prefill_tps,gen_tokens,gen_tps,gen_tps_ss,first_token_sec,kvcache_bytes | |
| 2048,2048,1110.75,128,25.31,25.78,0.1309,73363508 | |
| 4096,2048,1141.25,128,25.38,25.39,0.0419,120557620 | |
| 6144,2048,1110.00,128,24.98,25.00,0.0427,167751732 | |
| 8192,2048,1083.37,128,24.70,24.71,0.0436,214945844 | |
| 10240,2048,1073.62,128,24.37,24.38,0.0441,262139956 | |
| 12288,2048,1044.02,128,24.13,24.15,0.0445,309334068 | |
| 14336,2048,1022.83,128,23.84,23.85,0.0448,356528180 | |
| 16384,2048,997.48,128,23.48,23.48,0.0437,403722292 | |
| 18432,2048,977.30,128,23.22,23.23,0.0461,450916404 | |
| 20480,2048,963.75,128,22.92,22.93,0.0457,498110516 | |
| 22528,2048,941.93,128,22.57,22.58,0.0462,545304628 | |
| 24576,2048,918.37,128,22.31,22.32,0.0477,592498740 | |
| 26624,2048,904.81,128,22.00,22.01,0.0483,639692852 | |
| 28672,2048,884.27,128,21.73,21.73,0.0483,686886964 | |
| 30720,2048,866.40,128,21.50,21.51,0.0483,734081076 | |
| 32768,2048,854.75,128,21.34,21.35,0.0489,781275188 | |
| 34816,2048,838.28,128,21.02,21.03,0.0511,828469300 | |
| 36864,2048,822.56,128,20.72,20.73,0.0513,875663412 | |
| 38912,2048,815.12,128,20.51,20.52,0.0514,922857524 | |
| 40960,2048,802.29,128,20.29,20.30,0.0515,970051636 | |
| 43008,2048,787.48,128,20.10,20.11,0.0508,1017245748 | |
| 45056,2048,770.67,128,19.88,19.90,0.0537,1064439860 | |
| 47104,2048,759.52,128,19.58,19.59,0.0543,1111633972 | |
| 49152,2048,745.53,128,19.26,19.27,0.0540,1158828084 | |
| 51200,2048,737.76,128,19.23,19.24,0.0547,1206022196 | |
| 53248,2048,723.18,128,18.97,18.97,0.0543,1253216308 | |
| 55296,2048,713.04,128,18.73,18.73,0.0557,1300410420 | |
| 57344,2048,708.31,128,18.51,18.52,0.0564,1347604532 | |
| 59392,2048,693.08,128,18.31,18.32,0.0568,1394798644 | |
| 61440,2048,681.86,128,18.06,18.07,0.0565,1441992756 | |
| 63488,2048,674.66,128,18.01,18.01,0.0577,1489186868 | |
| 65536,2048,663.33,128,17.85,17.86,0.0586,1536380980 | |