Instructions to use NexaAI/octo-planner-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NexaAI/octo-planner-gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NexaAI/octo-planner-gguf", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("NexaAI/octo-planner-gguf", trust_remote_code=True, dtype="auto")

llama-cpp-python

How to use NexaAI/octo-planner-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NexaAI/octo-planner-gguf",
	filename="octopus-planning-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use NexaAI/octo-planner-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 NexaAI/octo-planner-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NexaAI/octo-planner-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 NexaAI/octo-planner-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NexaAI/octo-planner-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 NexaAI/octo-planner-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NexaAI/octo-planner-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 NexaAI/octo-planner-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NexaAI/octo-planner-gguf:Q4_K_M

Use Docker

docker model run hf.co/NexaAI/octo-planner-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use NexaAI/octo-planner-gguf with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/NexaAI/octo-planner-gguf:Q4_K_M

SGLang

How to use NexaAI/octo-planner-gguf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NexaAI/octo-planner-gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NexaAI/octo-planner-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NexaAI/octo-planner-gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NexaAI/octo-planner-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use NexaAI/octo-planner-gguf with Ollama:
```
ollama run hf.co/NexaAI/octo-planner-gguf:Q4_K_M
```

Unsloth Studio

How to use NexaAI/octo-planner-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 NexaAI/octo-planner-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 NexaAI/octo-planner-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NexaAI/octo-planner-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use NexaAI/octo-planner-gguf with Docker Model Runner:
```
docker model run hf.co/NexaAI/octo-planner-gguf:Q4_K_M
```

Lemonade

How to use NexaAI/octo-planner-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NexaAI/octo-planner-gguf:Q4_K_M

Run and chat with the model

lemonade run user.octo-planner-gguf-Q4_K_M

List all available models

lemonade list

Quantized Octo-planner: On-device Language Model for Planner-Action Agents Framework

This repo includes GGUF quantized models, for our Octo-planner model at NexaAIDev/octopus-planning

GGUF Quantization

To run the models, please download them to your local machine using either git clone or Hugging Face Hub

git clone https://huggingface.co/NexaAIDev/octo-planner-gguf

Run with llama.cpp (Recommended)

Clone and compile:

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
# Compile the source code:
make

Execute the Model:

Run the following command in the terminal:

./llama-cli -m ./path/to/octopus-planning-Q4_K_M.gguf -p "<|user|>Find my presentation for tomorrow's meeting, connect to the conference room projector via Bluetooth, increase the screen brightness, take a screenshot of the final summary slide, and email it to all participants<|end|><|assistant|>"

Run with Ollama

Since our models have not been uploaded to the Ollama server, please download the models and manually import them into Ollama by following these steps:

Install Ollama on your local machine. You can also following the guide from Ollama GitHub repository

git clone https://github.com/ollama/ollama.git ollama

Locate the local Ollama directory:

cd ollama

Create a Modelfile in your directory

touch Modelfile

In the Modelfile, include a FROM statement with the path to your local model, and the default parameters:

FROM ./path/to/octopus-planning-Q4_K_M.gguf

Use the following command to add the model to Ollama:

ollama create octopus-planning-Q4_K_M -f Modelfile

Verify that the model has been successfully imported:

ollama ls

Run the mode

ollama run octopus-planning-Q4_K_M "<|user|>Find my presentation for tomorrow's meeting, connect to the conference room projector via Bluetooth, increase the screen brightness, take a screenshot of the final summary slide, and email it to all participants<|end|><|assistant|>"

Quantized GGUF Models Benchmark

Name	Quant method	Bits	Size	Use Cases
octopus-planning-Q2_K.gguf	Q2_K	2	1.42 GB	fast but high loss, not recommended
octopus-planning-Q3_K.gguf	Q3_K	3	1.96 GB	extremely not recommended
octopus-planning-Q3_K_S.gguf	Q3_K_S	3	1.68 GB	extremely not recommended
octopus-planning-Q3_K_M.gguf	Q3_K_M	3	1.96 GB	moderate loss, not very recommended
octopus-planning-Q3_K_L.gguf	Q3_K_L	3	2.09 GB	not very recommended
octopus-planning-Q4_0.gguf	Q4_0	4	2.18 GB	moderate speed, recommended
octopus-planning-Q4_1.gguf	Q4_1	4	2.41 GB	moderate speed, recommended
octopus-planning-Q4_K.gguf	Q4_K	4	2.39 GB	moderate speed, recommended
octopus-planning-Q4_K_S.gguf	Q4_K_S	4	2.19 GB	fast and accurate, very recommended
octopus-planning-Q4_K_M.gguf	Q4_K_M	4	2.39 GB	fast, recommended
octopus-planning-Q5_0.gguf	Q5_0	5	2.64 GB	fast, recommended
octopus-planning-Q5_1.gguf	Q5_1	5	2.87 GB	very big, prefer Q4
octopus-planning-Q5_K.gguf	Q5_K	5	2.82 GB	big, recommended
octopus-planning-Q5_K_S.gguf	Q5_K_S	5	2.64 GB	big, recommended
octopus-planning-Q5_K_M.gguf	Q5_K_M	5	2.82 GB	big, recommended
octopus-planning-Q6_K.gguf	Q6_K	6	3.14 GB	very big, not very recommended
octopus-planning-Q8_0.gguf	Q8_0	8	4.06 GB	very big, not very recommended
octopus-planning-F16.gguf	F16	16	7.64 GB	extremely big

Quantized with llama.cpp

Downloads last month: 145

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit