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
Modelfilein your directory
touch Modelfile
- In the Modelfile, include a
FROMstatement 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
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