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metadata
language:
  - en
license: cc-by-nc-4.0
model_name: Octopus-V4-GGUF
base_model: NexaAIDev/Octopus-v4
inference: false
model_creator: NexaAIDev
quantized_by: Nexa AI, Inc.
tags:
  - function calling
  - on-device language model
  - gguf
  - llama cpp

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 and Zoey, for their extraordinary contributions to this quantization effort. Please explore Octopus-v4 for our original huggingface model.

(Recommended) Run with llama.cpp

  1. Clone and compile:
   git clone https://github.com/ggerganov/llama.cpp
   cd llama.cpp

   # Compile the source code:
   make
  1. Prepare the Input Prompt File:

Navigate to the prompt folder inside the llama.cpp, and create a new file named chat-with-octopus.txt.

chat-with-octopus.txt:

   # Write "User" at the top of the file to set the identifier for input.
   User: 
  1. Execute the Model:

Run the following command in the terminal:

   ./main -m ./Octopus-v4-gguf/Octopus-v4-Q2_K.gguf -c 512 -b 2048 -n 256 -t 1 --repeat_penalty 1.0 --top_k 0 --top_p 1.0 --color -i -r "User:" -f prompts/chat-with-octopus.txt

Example prompt to interact

  <|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

  1. Create a Modelfile in your directory and include a FROM statement with the path to your local model:
FROM ./path/to/octopus-v4-Q4_K_M
  1. Use the following command to add the model to Ollama:
ollama create octopus-v4-Q4_K_M -f Modelfile
  1. Verify that the model has been successfully imported:
ollama ls

Run the model

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 to evaluate the performances.
  • Evaluated with the Ollama 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