Instructions to use avar6/MiMo-V2.5-Pro-Base-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use avar6/MiMo-V2.5-Pro-Base-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="avar6/MiMo-V2.5-Pro-Base-gguf", filename="IQ2_S/MiMo-V2.5-Pro-Base-IQ2_S-00001-of-00008.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use avar6/MiMo-V2.5-Pro-Base-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 avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M
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 avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M
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 avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M
Use Docker
docker model run hf.co/avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use avar6/MiMo-V2.5-Pro-Base-gguf with Ollama:
ollama run hf.co/avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M
- Unsloth Studio
How to use avar6/MiMo-V2.5-Pro-Base-gguf with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for avar6/MiMo-V2.5-Pro-Base-gguf to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for avar6/MiMo-V2.5-Pro-Base-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for avar6/MiMo-V2.5-Pro-Base-gguf to start chatting
- Pi
How to use avar6/MiMo-V2.5-Pro-Base-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use avar6/MiMo-V2.5-Pro-Base-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 avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M
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 avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use avar6/MiMo-V2.5-Pro-Base-gguf with Docker Model Runner:
docker model run hf.co/avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M
- Lemonade
How to use avar6/MiMo-V2.5-Pro-Base-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull avar6/MiMo-V2.5-Pro-Base-gguf:Q4_K_M
Run and chat with the model
lemonade run user.MiMo-V2.5-Pro-Base-gguf-Q4_K_M
List all available models
lemonade list
Model
This repo contains specialized MoE-quants for MiMo-V2.5-Pro-Base. The idea being that given the huge size of the FFN tensors compared to the rest of the tensors in the model, it should be possible to achieve a better quality while keeping the overall size of the entire model smaller compared to a similar naive quantization. To that end, the quantization type default is kept in high quality and the FFN UP + FFN GATE tensors are quanted down along with the FFN DOWN tensors.
| Quant | Size | Mixture | PPL | 1-(Mean PPL(Q)/PPL(base)) | KLD |
|---|---|---|---|---|---|
| Q8_0 | 1012.92 GiB (8.50 BPW) | Q8_0 | 2.344588 ± 0.010019 | +0% | 0 |
| Q6_K | 787.32 GiB (6.61 BPW) | Q8_0 / Q6_K / Q6_K / Q6_K | 2.345036 ± 0.010024 | +0.0335% | 0.007237 ± 0.000078 |
| Q5_K_M | 704.84 GiB (5.92 BPW) | Q8_0 / Q5_K / Q5_K / Q6_K | 2.348139 ± 0.010046 | +0.1658% | 0.009220 ± 0.000084 |
| Q4_K_M | 586.58 GiB (4.92 BPW) | Q8_0 / Q4_K / Q4_K / Q5_K | 2.366152 ± 0.010162 | +0.9342% | 0.016214 ± 0.000138 |
| IQ4_XS | 454.99 GiB (3.82 BPW) | Q8_0 / IQ3_S / IQ3_S / IQ4_XS | 2.417036 ± 0.010382 | +3.1048% | 0.042863 ± 0.000354 |
| IQ3_S | 350.82 GiB (2.95 BPW) | Q6_K / IQ2_S / IQ2_S / IQ3_S | 2.638261 ± 0.011570 | +12.5417% | 0.126933 ± 0.000917 |
| IQ3_XS | 316.86 GiB (2.66 BPW) | Q6_K / IQ2_XS / IQ2_XS / IQ3_XXS | 2.819200 ± 0.012818 | +20.2601% | 0.188844 ± 0.001289 |
| IQ2_S | 299.70 GiB (2.52 BPW) | Q6_K / IQ2_XS / IQ2_XS / IQ2_S | 2.922672 ± 0.013398 | +24.6740% | 0.223651 ± 0.001460 |
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Model tree for avar6/MiMo-V2.5-Pro-Base-gguf
Base model
XiaomiMiMo/MiMo-V2.5-Pro-Base
