Instructions to use cortexso/falcon3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cortexso/falcon3 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cortexso/falcon3",
	filename="falcon3-10b-instruct-q2_k.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 cortexso/falcon3 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/falcon3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/falcon3:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/falcon3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/falcon3: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/falcon3:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cortexso/falcon3: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/falcon3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cortexso/falcon3:Q4_K_M

Use Docker

docker model run hf.co/cortexso/falcon3:Q4_K_M

LM Studio
Jan

vLLM

How to use cortexso/falcon3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cortexso/falcon3"
# 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/falcon3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cortexso/falcon3:Q4_K_M

Ollama
How to use cortexso/falcon3 with Ollama:
```
ollama run hf.co/cortexso/falcon3:Q4_K_M
```

Unsloth Studio new

How to use cortexso/falcon3 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/falcon3 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/falcon3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cortexso/falcon3 to start chatting

Pi new

How to use cortexso/falcon3 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf cortexso/falcon3: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": "falcon3"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use cortexso/falcon3 with Docker Model Runner:
```
docker model run hf.co/cortexso/falcon3:Q4_K_M
```

Lemonade

How to use cortexso/falcon3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cortexso/falcon3:Q4_K_M

Run and chat with the model

lemonade run user.falcon3-Q4_K_M

List all available models

lemonade list

Overview

Falcon3-10B-Instruct is part of the Falcon3 family of Open Foundation Models, offering state-of-the-art performance in reasoning, language understanding, instruction following, code, and mathematics. With 10 billion parameters, Falcon3-10B-Instruct is optimized for high-quality instruction-following tasks and supports multilingual capabilities in English, French, Spanish, and Portuguese. It provides a long context length of up to 32K tokens, making it suitable for extended document understanding and processing.

Variants

No	Variant	Cortex CLI command
1	Falcon3-10b	`cortex run falcon3:10b`

Use it with Jan (UI)

Install Jan using Quickstart
Use in Jan model Hub:
```
cortexhub/falcon3
```

Use it with Cortex (CLI)

Install Cortex using Quickstart
Run the model with command:
```
cortex run falcon3
```

Credits

Author: Falcon3 Team
Converter: Homebrew
Original License: License
Papers: Paper

Downloads last month: 47

GGUF

Model size

10B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Paper for cortexso/falcon3

The Falcon Series of Open Language Models

Paper • 2311.16867 • Published Nov 28, 2023 • 15