--- title: octo-net-gguf canonical_url: "https://www.modelscope.ai/models/NexaAI/octo-net-gguf" md_url: "https://www.modelscope.ai/models/NexaAI/octo-net-gguf.md" repository: NexaAI/octo-net-gguf last_updated: 2025-11-25 license: cc-by-nc-4.0 base_model: - NexaAIDev/Octopus-v4 base_model_relation: quantized library_name: - gguf - pytorch frameworks: - pytorch language: - en downloads: 191 stars: 0 tags: - "function calling" - "on-device language model" - gguf - "llama cpp" --- # octo-net-gguf > octo-net-gguf - An open-source model by NexaAI on ModelScope. Octopus V4-GGUF: Graph of language models NexaAI/octo-net-gguf is a machine learning model on ModelScope. licensed under cc-by-nc-4.0. derived from NexaAIDev/Octopus-v4. - **Repository**: NexaAI/octo-net-gguf - **License**: cc-by-nc-4.0 - **Base model**: NexaAIDev/Octopus-v4 - **Tags**: function calling, on-device language model, gguf, llama cpp - **Downloads**: 191 - **Stars**: 0 - **Last updated**: 2025-11-25 Source: https://www.modelscope.ai/models/NexaAI/octo-net-gguf --- # Octopus V4-GGUF: Graph of language models

- Original Model - Nexa AI Website - Octopus-v4 Github - ArXiv - Domain LLM Leaderbaord

nexa-octopus

**Acknowledgement**: We sincerely thank our community members, [Mingyuan](https://huggingface.co/ThunderBeee) and [Zoey](https://huggingface.co/ZY6), for their extraordinary contributions to this quantization effort. Please explore [Octopus-v4](https://huggingface.co/NexaAIDev/Octopus-v4) for our original huggingface model. ## Get Started To run the models, please download them to your local machine using either git clone or [Hugging Face Hub](https://huggingface.co/docs/huggingface_hub/en/guides/download) ``` git clone https://huggingface.co/NexaAIDev/octopus-v4-gguf ``` ## Run with [llama.cpp](https://github.com/ggerganov/llama.cpp) (Recommended) 1. **Clone and compile:** ```bash git clone https://github.com/ggerganov/llama.cpp cd llama.cpp # Compile the source code: make ``` 2. **Execute the Model:** Run the following command in the terminal: ```bash ./main -m ./path/to/octopus-v4-Q4_K_M.gguf -n 256 -p "<|system|>You are a router. Below is the query from the users, please call the correct function and generate the parameters to call the function.<|end|><|user|>Tell me the result of derivative of x^3 when x is 2?<|end|><|assistant|>" ``` ## Run with [Ollama](https://github.com/ollama/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: 1. Install Ollama on your local machine. You can also following the guide from [Ollama GitHub repository](https://github.com/ollama/ollama/blob/main/docs/import.md) ```bash git clone https://github.com/ollama/ollama.git ollama ``` 2. Locate the local Ollama directory: ```bash cd ollama ``` 3. Create a `Modelfile` in your directory ```bash touch Modelfile ``` 4. In the Modelfile, include a `FROM` statement with the path to your local model, and the default parameters: ```bash FROM ./path/to/octopus-v4-Q4_K_M.gguf PARAMETER temperature 0 PARAMETER num_ctx 1024 PARAMETER stop ``` 5. Use the following command to add the model to Ollama: ```bash ollama create octopus-v4-Q4_K_M -f Modelfile ``` 6. Verify that the model has been successfully imported: ```bash ollama ls ``` 7. Run the model ```bash ollama run octopus-v4-Q4_K_M "<|system|>You are a router. Below is the query from the users, please call the correct function and generate the parameters to call the function.<|end|><|user|>Tell me the result of derivative of x^3 when x is 2?<|end|><|assistant|>" ``` ### Dataset and Benchmark * Utilized questions from [MMLU](https://github.com/hendrycks/test) to evaluate the performances. * Evaluated with the Ollama [llm-benchmark](https://github.com/MinhNgyuen/llm-benchmark) method. ## Quantized GGUF Models | Name | Quant method | Bits | Size | Respons (token/second) | Use Cases | | ---------------------- | ------------ | ---- | ------- | ---------------------- | ----------------------------------------- | | Octopus-v4.gguf | | | 7.64 GB | 27.64 | extremely large | | Octopus-v4-Q2_K.gguf | Q2_K | 2 | 1.42 GB | 54.20 | extremely not recommended, high loss | | Octopus-v4-Q3_K.gguf | Q3_K | 3 | 1.96 GB | 51.22 | not recommended | | Octopus-v4-Q3_K_S.gguf | Q3_K_S | 3 | 1.68 GB | 51.78 | not very recommended | | Octopus-v4-Q3_K_M.gguf | Q3_K_M | 3 | 1.96 GB | 50.86 | not very recommended | | Octopus-v4-Q3_K_L.gguf | Q3_K_L | 3 | 2.09 GB | 50.05 | not very recommended | | Octopus-v4-Q4_0.gguf | Q4_0 | 4 | 2.18 GB | 65.76 | good quality, recommended | | Octopus-v4-Q4_1.gguf | Q4_1 | 4 | 2.41 GB | 69.01 | slow, good quality, recommended | | Octopus-v4-Q4_K.gguf | Q4_K | 4 | 2.39 GB | 55.76 | slow, good quality, recommended | | Octopus-v4-Q4_K_S.gguf | Q4_K_S | 4 | 2.19 GB | 53.98 | high quality, recommended | | Octopus-v4-Q4_K_M.gguf | Q4_K_M | 4 | 2.39 GB | 58.39 | some functions loss, not very recommended | | Octopus-v4-Q5_0.gguf | Q5_0 | 5 | 2.64 GB | 61.98 | slow, good quality | | Octopus-v4-Q5_1.gguf | Q5_1 | 5 | 2.87 GB | 63.44 | slow, good quality | | Octopus-v4-Q5_K.gguf | Q5_K | 5 | 2.82 GB | 58.28 | moderate speed, recommended | | Octopus-v4-Q5_K_S.gguf | Q5_K_S | 5 | 2.64 GB | 59.95 | moderate speed, recommended | | Octopus-v4-Q5_K_M.gguf | Q5_K_M | 5 | 2.82 GB | 53.31 | fast, good quality, recommended | | Octopus-v4-Q6_K.gguf | Q6_K | 6 | 3.14 GB | 52.15 | large, not very recommended | | Octopus-v4-Q8_0.gguf | Q8_0 | 8 | 4.06 GB | 50.10 | very large, good quality | | Octopus-v4-f16.gguf | f16 | 16 | 7.64 GB | 30.61 | extremely large | _Quantized with llama.cpp_