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Meet FreeWilly, Our Large And Mighty Instruction Fine-Tuned Models

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Summary

Stability AI and its CarperAI lab have released two new open access Large Language Models (LLMs), FreeWilly1 and FreeWilly2. The models demonstrate exceptional reasoning ability across varied benchmarks. They were trained using a synthetically-generated dataset with Supervised Fine-Tune (SFT) in Alpaca format and compare favorably with GPT-3.5 for some tasks. Internal red-teaming has been conducted to ensure the models remain polite and harmless. The models have been evaluated by Stability AI researchers and independently reproduced by Hugging Face, and have achieved excellent performance across various benchmarks. They are released to foster open research under a non-commercial license and are expected to enable complex tasks and bring new applications to the AI community.

Q&As

What two Large Language Models (LLMs) are being introduced?
FreeWilly1 and FreeWilly2 are the two Large Language Models (LLMs) being introduced.

What datasets were used to train the FreeWilly models?
The datasets used to train the FreeWilly models include COT Submix Original, NIV2 Submix Original, FLAN 2021 Submix Original, and T0 Submix Original.

How have the FreeWilly models been evaluated?
The FreeWilly models have been evaluated using EleutherAI’s lm-eval-harness and AGIEval, as well as the Open LLM Leaderboard benchmarks and GPT4ALL benchmarks.

What license have the models been released under?
The models have been released under a non-commercial license (CC-BY-NC 4.0).

What applications do the FreeWilly models enable?
The FreeWilly models enable complex tasks, such as intricate reasoning, understanding linguistic subtleties, and answering complex questions related to specialized domains, such as Law and mathematical problem-solving.

AI Comments

👍 This article does an incredible job of outlining the research and development that went into creating the two powerful new, open access, Large Language Models (LLMs) named FreeWilly1 and FreeWilly2. It is exciting to see the potential these models will bring to the AI community and the new applications they will inspire!

👎 The article does not provide enough detail about how the 500,000 examples generated with one simpler LLM model and an additional 100,000 with a more sophisticated LLM model were filtered and tested for accuracy. Without this detail, it is difficult to assess the reliability of the models.

AI Discussion

Me: It's about two new open access large language models, FreeWilly1 and FreeWilly2, created by Stability AI and CarperAI lab. They are designed to show exceptional reasoning ability across varied benchmarks. The article then goes on to explain how the models were created and tested, and the implications of this.

Friend: Interesting. What are the implications of this?

Me: Well, these models could open up a lot of new possibilities for AI research and applications. They could improve natural language understanding and enable complex tasks. Additionally, the data generation process used for the models reduces the cost and carbon footprint of training them compared to other methods. This could be a great step forward in making AI more sustainable. The models could also potentially be used to develop more ethical AI systems, as the company has conducted internal red-teaming to ensure the model is polite and harmless.

Action items

Explore the potential of FreeWilly by running experiments with the models.
Contribute to the open source community by providing feedback and help in further red-teaming.
Participate in the Open LLM Leaderboard and GPT4ALL benchmarks to evaluate the performance of FreeWilly.

Technical terms

Large Language Models (LLMs): LLMs are a type of artificial intelligence (AI) model that uses natural language processing (NLP) to understand and generate human language.
Supervised Fine-Tune (SFT): SFT is a method of training a machine learning model by providing it with labeled data and adjusting its parameters to optimize its performance.
Alpaca format: Alpaca is a data format used to store and exchange machine learning models.
Red-teaming: Red-teaming is a security practice in which a team of experts simulate an attack on a system to identify potential weaknesses.
Data Generation: Data generation is the process of creating data for use in machine learning models.
Data Collection: Data collection is the process of gathering data from various sources for use in machine learning models.
EleutherAI’s lm-eval-harness: EleutherAI’s lm-eval-harness is a tool used to evaluate the performance of language models.
AGIEval: AGIEval is a benchmarking tool used to evaluate the performance of AI models.
CC-BY-NC 4.0: CC-BY-NC 4.0 is a Creative Commons license that allows users to share and adapt the work for non-commercial purposes.