GGUF
English
coder
Qwenn
ollama
llama.cpp
Smart
Agent
Coding
developer-tools
developer
local-ai
imatrix
conversational
Instructions to use midnightcoderagent/MidnightCoder-80B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use midnightcoderagent/MidnightCoder-80B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="midnightcoderagent/MidnightCoder-80B", filename="MidnightCoder-80B.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 midnightcoderagent/MidnightCoder-80B 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 midnightcoderagent/MidnightCoder-80B # Run inference directly in the terminal: llama cli -hf midnightcoderagent/MidnightCoder-80B
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf midnightcoderagent/MidnightCoder-80B # Run inference directly in the terminal: llama cli -hf midnightcoderagent/MidnightCoder-80B
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 midnightcoderagent/MidnightCoder-80B # Run inference directly in the terminal: ./llama-cli -hf midnightcoderagent/MidnightCoder-80B
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 midnightcoderagent/MidnightCoder-80B # Run inference directly in the terminal: ./build/bin/llama-cli -hf midnightcoderagent/MidnightCoder-80B
Use Docker
docker model run hf.co/midnightcoderagent/MidnightCoder-80B
- LM Studio
- Jan
- Ollama
How to use midnightcoderagent/MidnightCoder-80B with Ollama:
ollama run hf.co/midnightcoderagent/MidnightCoder-80B
- Unsloth Studio
How to use midnightcoderagent/MidnightCoder-80B 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 midnightcoderagent/MidnightCoder-80B 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 midnightcoderagent/MidnightCoder-80B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for midnightcoderagent/MidnightCoder-80B to start chatting
- Pi
How to use midnightcoderagent/MidnightCoder-80B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf midnightcoderagent/MidnightCoder-80B
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": "midnightcoderagent/MidnightCoder-80B" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use midnightcoderagent/MidnightCoder-80B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf midnightcoderagent/MidnightCoder-80B
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 midnightcoderagent/MidnightCoder-80B
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use midnightcoderagent/MidnightCoder-80B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf midnightcoderagent/MidnightCoder-80B
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 "midnightcoderagent/MidnightCoder-80B" \ --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"
- Docker Model Runner
How to use midnightcoderagent/MidnightCoder-80B with Docker Model Runner:
docker model run hf.co/midnightcoderagent/MidnightCoder-80B
- Lemonade
How to use midnightcoderagent/MidnightCoder-80B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull midnightcoderagent/MidnightCoder-80B
Run and chat with the model
lemonade run user.MidnightCoder-80B-{{QUANT_TAG}}List all available models
lemonade list
| license: apache-2.0 | |
| language: | |
| - en | |
| base_model: | |
| - Qwen/Qwen3-Coder-Next | |
| tags: | |
| - coder | |
| - Qwenn | |
| - gguf | |
| - ollama | |
| - llama.cpp | |
| - Smart | |
| - Agent | |
| - Coding | |
| - developer-tools | |
| - developer | |
| - local-ai | |
| ````markdown | |
| MidnightCoder-80B is designed for developers who want to run a powerful coding model locally using llama.cpp, Ollama, LM Studio, or any GGUF-compatible inference engine. | |
| This distribution is optimized for the Midnight Coder agent but is fully compatible with any coding agent or workflow. It excels at structured, specification-driven software engineering while remaining suitable for general-purpose coding tasks. | |
| Run directly with Ollama | |
| You can download and run MidnightCoder-80B directly from Ollama: | |
| You can download and run MidnightCoder-80B directly from Ollama: | |
| ```bash | |
| ollama run midnightcoderagent/MidnightCoder-80B | |
| ``` | |
| Or pull it first: | |
| ```bash | |
| ollama pull midnightcoderagent/MidnightCoder-80B | |
| ``` | |
| Then run it anytime: | |
| ```bash | |
| ollama run midnightcoderagent/MidnightCoder-80B | |
| ``` | |
| The model will be downloaded automatically the first time and cached locally for future use. | |
| --- | |
| π Midnight Coder Agent | |
| GitHub: https://github.com/midnightcoderagent/Midnight-Coder | |
| Website: https://midnightcoderagent.github.io | |
| Install: npm install -g midnight-coder (Linux support currently available. Windows and macOS support coming soon.) | |
| Issues & Feature Requests: https://github.com/midnightcoderagent/Midnight-Coder/issues | |
| ## Download the GGUF | |
| If you prefer using **llama.cpp**, **LM Studio**, or another GGUF-compatible runtime, you can download the GGUF files directly from this repository. | |
| --- | |
| # π SmartContext Optimization | |
| One of the flagship features of **Midnight Coder** is **SmartContext**. | |
| Instead of sending the entire conversation and every project file to the language model, SmartContext intelligently analyzes the current task and forwards only the information that is actually relevant. | |
| In real-world software engineering workflows, SmartContext reduces the amount of context sent from **Midnight Coder** to the model by approximately **45%** while maintaining coding quality. | |
| Benefits include: | |
| - β‘ Around **45% less prompt context** | |
| - π Faster agent iterations | |
| - πΎ Lower token usage | |
| - π Better handling of large repositories | |
| - π§ More efficient use of long-context models | |
| - π₯ Excellent local performance, even on older hardware | |
| SmartContext is implemented entirely by the **Midnight Coder** agent, requiring no modifications to the model itself. | |
| ```` |