Octopus v2 is a 2B model that calls Android APIs with one token per function

Wei Chen and Zhiyuan Li published Octopus v2 on April 2, 2024, a 2B function-calling model fine-tuned from Google’s Gemma 2B that turns a spoken or typed request into an Android API call. On their own evaluation set built from 20 Android APIs, the authors report 99.524% accuracy and 0.38 seconds per call for Octopus v2, against 98.571% and 1.02 seconds for GPT-4. Both authors give Stanford University as their affiliation on the paper and publish the weights under the name Nexa AI.

The distinguishing idea is what the authors call a functional token. Each of the 20 APIs gets its own special token added to the tokenizer and to the model’s output head, so picking a function becomes a single choice among 20 token candidates instead of writing the function name out as text and hoping every piece of it lands correctly. Since the model learned during fine-tuning what each of those tokens stands for, the function descriptions no longer have to sit in the prompt, which the paper puts at over 95% less context. Retrieval-augmented function calling processes roughly 1,000 tokens per call in their comparison, while Octopus v2 sees the query alone, typically under 30 tokens.

Shorter context is also where the speed comes from. The authors measure Llama-7B with retrieval-augmented function calling at 68.095% accuracy and 13.46 seconds per call on a single NVIDIA A100, and Octopus v2 at 0.38 seconds under the same settings on the same A100, a 35-fold difference. The phone figure is a separate measurement. With the model quantised and the state for the fixed prompt prefix precomputed, they report a function call finishing in 1.1 to 1.7 seconds for queries of 20 to 30 tokens on what the paper calls a standard Android phone, without naming the device, and they estimate 37 times more calls per iPhone battery charge than a 7B model with retrieval.

The authors name limits of their own result. Training with LoRA instead of the full model drops accuracy to 99.048%, and cutting the training set from 1,000 to 100 generated examples per API drops it to 98.095%, which they still put forward as the low end of a usable range. The set of callable functions is fixed at training time, since every API has to exist as a token before the model can emit it, and they price the data generation at 0.0224 USD per 1,000 examples per API. They also note that the GPT-3.5 and GPT-4 latencies were taken through OpenAI’s API on March 18, 2024, and may reflect API traffic or hardware allocation at that moment.

Nexa AI released the weights on Hugging Face under a non-commercial CC BY-NC 4.0 license, on top of Google’s Gemma 2B. The model card said at the time that the team was still preparing the code for an open-source release.