gorilla VS poozle

Retrieval-Augmented Fine-Tuning is a really promising technique.

FTA:

> Tianjun and Shishir were looking to improve these deficiencies of RAG. They hypothesized that a student who studies the textbooks before the open-book exam would be more likely to perform better than a student who references the textbook only during the exam. Translating that back to LLMs, if a model “studied” the documents beforehand, could that improve its RAG performance?

Incidentally, the team who wrote the paper released some nice code to generate domain-specific fine-tuning datasets: https://github.com/ShishirPatil/gorilla/tree/main/raft

Do you leverage https://gorilla.cs.berkeley.edu/ at all? If not, perhaps consider if it would solve some pain for you.

* Gorilla [https://github.com/ShishirPatil/gorilla]

Could be interesting to try some of these exercises with these models.

gorilla

Nice this made me go back and check up on the Gorilla LLM project [1] to see whats they are doing with API and if they have applied their fine tuning to any of the newer foundation models but looks like things have slowed down since they launched (?) or maybe development is happening elsewhere on some invisible discord channel but I hope the intersection of API calling and LLM as a logic processing function keep getting focus it's an important direction for interop across the web.

[1] https://github.com/ShishirPatil/gorilla

"Gorilla: Large Language Model Connected with Massive APIs" (2023) https://gorilla.cs.berkeley.edu/ :

> Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. We also release APIBench, the largest collection of APIs, curated and easy to be trained on! Join us, as we try to expand the largest API store and teach LLMs how to write them!

eval/:

We do the ETL in the background and ensure that the model is completed irrespective of the 1-1 or different entity. Anyways you would also be doing the same.

Also if you need more customization you can write on top of the integration we have already (https://github.com/poozlehq/poozle/tree/main/integrations/ti...) and plug it into the platform.