Instructions to use cortexso/opencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use cortexso/opencoder 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 cortexso/opencoder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cortexso/opencoder:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cortexso/opencoder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cortexso/opencoder: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 cortexso/opencoder:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cortexso/opencoder: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 cortexso/opencoder:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cortexso/opencoder:Q4_K_M

Use Docker

docker model run hf.co/cortexso/opencoder:Q4_K_M

LM Studio
Jan

vLLM

How to use cortexso/opencoder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cortexso/opencoder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cortexso/opencoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cortexso/opencoder:Q4_K_M

Ollama
How to use cortexso/opencoder with Ollama:
```
ollama run hf.co/cortexso/opencoder:Q4_K_M
```

Unsloth Studio

How to use cortexso/opencoder 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 cortexso/opencoder 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 cortexso/opencoder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cortexso/opencoder to start chatting

Atomic Chat new
Docker Model Runner
How to use cortexso/opencoder with Docker Model Runner:
```
docker model run hf.co/cortexso/opencoder:Q4_K_M
```

Lemonade

How to use cortexso/opencoder with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cortexso/opencoder:Q4_K_M

Run and chat with the model

lemonade run user.opencoder-Q4_K_M

List all available models

lemonade list

Overview

OpenCoder is an open and reproducible code LLM family, featuring 1.5B and 8B base and chat models that support both English and Chinese languages. Built from scratch, OpenCoder is pretrained on 2.5 trillion tokens, composed of 90% raw code and 10% code-related web data. It undergoes supervised fine-tuning (SFT) with over 4.5 million high-quality examples, achieving performance on par with top-tier code LLMs

Variants

No	Variant	Cortex CLI command
1	Opencoder-8b	`cortex run opencoder:8b`

Use it with Jan (UI)

Install Jan using Quickstart
Use in Jan model Hub:
```
cortexhub/opencoder
```

Use it with Cortex (CLI)

Install Cortex using Quickstart
Run the model with command:
```
cortex run opencoder
```

Credits

Author: inftech.ai
Converter: Homebrew
Original License: Licence
Papers: Paper

Downloads last month: 95

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Paper for cortexso/opencoder

OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models

Paper • 2411.04905 • Published Nov 7, 2024 • 128