Zack Zhiyuan Li
commited on
Commit
•
cf57a97
1
Parent(s):
9be03ce
add logo
Browse files
README.md
CHANGED
@@ -21,9 +21,8 @@ language:
|
|
21 |
</p>
|
22 |
|
23 |
<p align="center" width="100%">
|
24 |
-
<
|
25 |
</p>
|
26 |
-
|
27 |
## Introducing Octopus-V2-2B
|
28 |
Octopus-V2-2B, an advanced open-source language model with 2 billion parameters, represents Nexa AI's research breakthrough in the application of large language models (LLMs) for function calling, specifically tailored for Android APIs. Unlike Retrieval-Augmented Generation (RAG) methods, which require detailed descriptions of potential function arguments—sometimes needing up to tens of thousands of input tokens—Octopus-V2-2B introduces a unique **functional token** strategy for both its training and inference stages. This approach not only allows it to achieve performance levels comparable to GPT-4 but also significantly enhances its inference speed beyond that of RAG-based methods, making it especially beneficial for edge computing devices.
|
29 |
|
|
|
21 |
</p>
|
22 |
|
23 |
<p align="center" width="100%">
|
24 |
+
<a><img src="Octopus-logo.jpeg" alt="nexa-octopus" style="width: 40%; min-width: 300px; display: block; margin: auto;"></a>
|
25 |
</p>
|
|
|
26 |
## Introducing Octopus-V2-2B
|
27 |
Octopus-V2-2B, an advanced open-source language model with 2 billion parameters, represents Nexa AI's research breakthrough in the application of large language models (LLMs) for function calling, specifically tailored for Android APIs. Unlike Retrieval-Augmented Generation (RAG) methods, which require detailed descriptions of potential function arguments—sometimes needing up to tens of thousands of input tokens—Octopus-V2-2B introduces a unique **functional token** strategy for both its training and inference stages. This approach not only allows it to achieve performance levels comparable to GPT-4 but also significantly enhances its inference speed beyond that of RAG-based methods, making it especially beneficial for edge computing devices.
|
28 |
|