Instructions to use buley/cog-360m-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use buley/cog-360m-instruct-gguf 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 buley/cog-360m-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf buley/cog-360m-instruct-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf buley/cog-360m-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf buley/cog-360m-instruct-gguf: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 buley/cog-360m-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf buley/cog-360m-instruct-gguf: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 buley/cog-360m-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf buley/cog-360m-instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/buley/cog-360m-instruct-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use buley/cog-360m-instruct-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "buley/cog-360m-instruct-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "buley/cog-360m-instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/buley/cog-360m-instruct-gguf:Q4_K_M

Ollama
How to use buley/cog-360m-instruct-gguf with Ollama:
```
ollama run hf.co/buley/cog-360m-instruct-gguf:Q4_K_M
```

Unsloth Studio

How to use buley/cog-360m-instruct-gguf 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 buley/cog-360m-instruct-gguf 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 buley/cog-360m-instruct-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for buley/cog-360m-instruct-gguf to start chatting

Docker Model Runner
How to use buley/cog-360m-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/buley/cog-360m-instruct-gguf:Q4_K_M
```

Lemonade

How to use buley/cog-360m-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull buley/cog-360m-instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.cog-360m-instruct-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

Cog 360M Instruct

Forkjoin.ai conversion of cog-360m-instruct-gguf to GGUF format for edge deployment.

Model Details

Source Model: See upstream
Format: GGUF
Converted by: Forkjoin.ai

Usage

With llama.cpp

./llama-cli -m cog-360m-instruct-gguf.gguf -p "Your prompt here" -n 256

With Ollama

Create a Modelfile:

FROM ./cog-360m-instruct-gguf.gguf

ollama create cog-360m-instruct-gguf -f Modelfile
ollama run cog-360m-instruct-gguf

About Forkjoin.ai

Forkjoin.ai runs AI models at the edge -- in-browser, on-device, zero cloud cost. These converted models power real-time inference, speech recognition, and natural language capabilities.

All conversions are optimized for edge deployment within browser and mobile memory constraints.

License

Apache 2.0 (follows upstream model license)

Downloads last month: 17

GGUF

Model size

0.4B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

16-bit