Text Generation
GGUF
English
German
mimi
tool-calling
function-calling
agent
fine-tuned
wllama
browser-inference
on-device-ai
local-ai
privacy-first
Eval Results (legacy)
conversational
Instructions to use MimiTechAI/mimi-pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use MimiTechAI/mimi-pro with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="MimiTechAI/mimi-pro", filename="mimi-qwen3-4b-q4km.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use MimiTechAI/mimi-pro with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MimiTechAI/mimi-pro # Run inference directly in the terminal: llama-cli -hf MimiTechAI/mimi-pro
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MimiTechAI/mimi-pro # Run inference directly in the terminal: llama-cli -hf MimiTechAI/mimi-pro
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 MimiTechAI/mimi-pro # Run inference directly in the terminal: ./llama-cli -hf MimiTechAI/mimi-pro
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 MimiTechAI/mimi-pro # Run inference directly in the terminal: ./build/bin/llama-cli -hf MimiTechAI/mimi-pro
Use Docker
docker model run hf.co/MimiTechAI/mimi-pro
- LM Studio
- Jan
- vLLM
How to use MimiTechAI/mimi-pro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MimiTechAI/mimi-pro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MimiTechAI/mimi-pro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MimiTechAI/mimi-pro
- Ollama
How to use MimiTechAI/mimi-pro with Ollama:
ollama run hf.co/MimiTechAI/mimi-pro
- Unsloth Studio new
How to use MimiTechAI/mimi-pro 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 MimiTechAI/mimi-pro 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 MimiTechAI/mimi-pro to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MimiTechAI/mimi-pro to start chatting
- Pi new
How to use MimiTechAI/mimi-pro with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf MimiTechAI/mimi-pro
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": "MimiTechAI/mimi-pro" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use MimiTechAI/mimi-pro with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf MimiTechAI/mimi-pro
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 MimiTechAI/mimi-pro
Run Hermes
hermes
- Docker Model Runner
How to use MimiTechAI/mimi-pro with Docker Model Runner:
docker model run hf.co/MimiTechAI/mimi-pro
- Lemonade
How to use MimiTechAI/mimi-pro with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MimiTechAI/mimi-pro
Run and chat with the model
lemonade run user.mimi-pro-{{QUANT_TAG}}List all available models
lemonade list
| { | |
| "alora_invocation_tokens": null, | |
| "alpha_pattern": {}, | |
| "arrow_config": null, | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "Qwen/Qwen3-4B", | |
| "bias": "none", | |
| "corda_config": null, | |
| "ensure_weight_tying": false, | |
| "eva_config": null, | |
| "exclude_modules": null, | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layer_replication": null, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 128, | |
| "lora_bias": false, | |
| "lora_dropout": 0.05, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "peft_version": "0.18.1", | |
| "qalora_group_size": 16, | |
| "r": 64, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": [ | |
| "gate_proj", | |
| "q_proj", | |
| "o_proj", | |
| "up_proj", | |
| "v_proj", | |
| "down_proj", | |
| "k_proj" | |
| ], | |
| "target_parameters": null, | |
| "task_type": "CAUSAL_LM", | |
| "trainable_token_indices": null, | |
| "use_dora": false, | |
| "use_qalora": false, | |
| "use_rslora": false | |
| } |