---
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.

---

🔗 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.

---

# 🚀 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:

- ⚡ Around **45% less prompt context**
- 🚀 Faster agent iterations
- 💾 Lower token usage
- 📂 Better handling of large repositories
- 🧠 More efficient use of long-context models
- 🔥 Excellent local performance, even on older hardware

SmartContext is implemented entirely by the **Midnight Coder** agent, requiring no modifications to the model itself.
````