Blaed

joined 2 years ago
MODERATOR OF
 

cross-posted from: https://lemmy.world/post/6399678

πŸ€– Happy FOSAI Friday! πŸš€

Friday, October 6, 2023

HyperTech News Report #0003

Hello Everyone!

This week highlights a wave of new papers and frameworks that expand upon LLM functionalities. With a tsunami of applications on the horizon I foresee a bedrock of tools to preceed. I'm not sure what kits and processes will end up part of this bedrock, but I hope some of these methods end up interesting or helpful to your workflow!

Table of Contents

Community Changelog

Image of the Week

This image of the week comes from one of my own projects! I hope you don't mind me sharing.. I was really happy with this result. This was generated from an SDXL model I trained and host on Replicate. I use an mock ensemble approach to generate various game assets for an experimental roguelike I'm making with a colleague.

My current method is not at all efficient, but I have fun. Right now, I have three SDXL models I interact with, each generating art I can use for my project. Andraxus takes care of wallpapers and in-game levels (this image you're seeing here), his in-game companion Biazera imagines characters and entities of this world, while Cerephelo tinkers and toils over the machinations within - crafting items, loot, powerups, etc.

I've been hesitant self-promoting here. But if there's genuine interest in this project I would be more than happy sharing more details. It's still in pre-alpha development, but there were plans releasing all of the models we use as open-source (obviously). We're still working on the engine though. Let me know if you want to see more on this project.


News


  1. Arxiv Publications Workflow: A new workflow has been introduced that allows users to scrape search topics from Arxiv, converting the results into markdown (MD) format. This makes it easier to digest and understand topics from Arxiv published content. The tool, available on GitHub, is particularly useful for those who wish to delve deeper into research papers and run their own research processes.

  2. Texting LLMs from Your Phone: A guide has been shared that enables users to communicate with their personal assistants via simple text messages. The process involves setting up a Twilio account, purchasing and registering a phone number, and then integrating it with the Replicate platform. The code, available on GitHub, makes it possible to send and receive messages from LLMs directly on one's phone.

  3. Microsoft's AutoGen: Microsoft has released AutoGen, a tool designed to aid in the creation of autonomous LLM agents. Compatible with ChatGPT models, AutoGen facilitates the development of LLM applications using multiple agents that can converse with each other to solve tasks. The framework is customizable and allows for seamless human participation. More details can be found on GitHub.

  4. Promptbench and ACE Framework: Promptbench is a new project focused on the evaluation and benchmarking of models. Stemming from the DyVal paper, it aims to provide reliable insights into model performance. On the other hand, the ACE Framework, designed for autonomous cognitive entities, offers a unique approach to agent tooling. While still in its early stages, it promises to bring about innovative implementations in the realms of personal assistants, game world NPCs, autonomous employees, and embodied robots.

  5. Research Highlights: Several papers have been published that delve into the intricacies of LLMs. One paper introduces a method to enhance the zero-shot reasoning abilities of LLMs, while another, titled DyVal, proposes a dynamic evaluation protocol for LLMs. Additionally, the concept of Low-Rank Adapters (LoRA) ensembles for LLM fine-tuning has been explored, emphasizing the potential of using one model and dynamically swapping the fine-tuned QLoRA adapters.


Tools & Frameworks


Keep Up w/ Arxiv Publications

Due to a drastic change in personal and work schedules, I've had to shift how I research and develop posts and projects for you guys. That being said, I found this workflow from the same author of the ACE Framework particularly helpful. It scrapes a search topic from Arxiv and returns a massive XML that is converted to markdown (MD) to then be used as an injectable context report for a LLM of your choosing (to further break down and understand topics) or as a well of information for the classic CTRL + F search. But at this point, info is aggregated (and human readable) from Arxiv published content.

After reading abstractions you can further drill into each paper and dissect / run your own research processes as you see fit. There is definitely more room for automation and organization here I'm sure, but this has been a big resource for me lately so I wanted to proliferate it for others who might find it helpful too.

Text LLMs from Your Phone

I had an itch to make my personal assistants more accessible - so I started investigating ways I could simply text them from my iPhone (via simple sms). There are many other ways I could've done this, but texting has been something I always like to default to in communications. So, I found this cool guide that uses infra I already prefer (Replicate) and has a bonus LangChain integration - which opens up the door to a ton of other opportunities down the line.

This tutorial was pretty straightforward - but to be honest, making the Twilio account, buying a phone number (then registering it) took the longest. The code itself takes less than 10 minutes to get up and running with ngrok. Super simple and straightforward there. The Twilio process? Not so much.. but it was worth the pain!

I am still waiting on my phone number to be verified (so that the Replicate inference endpoint can actually send SMS back to me) but I ended the night successfully texting the server on my local PC. It was wild texting the Ahsoka example from my phone and seeing the POST response return (even though it didn't go through SMS I could still see the server successfully receive my incoming message/prompt). I think there's a lot of fun to be had giving casual phone numbers and personalities to assistants like this. Especially if you want to LangChain some functions beyond just the conversation. If there's more interest on this topic, I can share how my assistant evolves once it gets full access to return SMS. I am designing this to streamline my personal life, and if it proves to be useful I will absolutely release the project as open-source.

AutoGen

With Agents on the rise, tools and automation pipelines to build them have become increasingly more important to consider. It seems like Microsoft is well aware of this, and thus released AutoGen, a tool to help enable this automation tooling and creation of autonomous LLM agents. AutoGen is compatible with ChatGPT models and is being kitted for local LLMs as we speak.

AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.

Promptbench

I recently found promptbench - a project that seems to have stemmed from the DyVal paper (shared below). I for one appreciate some of the new tools that are releasing focused around the evaluation and benchmarking of models. I hope we continue to see more evals, benchmarks, and projects that return us insights we can rely upon.

ACE Framework

A new framework has been proposed and designed for autonomous cognitive entities. This appears similar to agents and their style of tooling, but with a different architecture approach? I don't believe implementation of this is ready, but it may be soon and something to keep an eye on.

There are many possible implementations of the ACE Framework. Rather than detail every possible permutation, here is a list of categories that we perceive as likely and viable.

Personal Assistant and/or Companion

  • This is a self-contained version of ACE that is intended to interact with one user.
  • Think of Cortana from HALO, Samantha from HER, or Joi from Blade Runner 2049. (yes, we recognize these are all sexualized female avatars)
  • The idea would be to create something that is effectively a personal Executive Assistant that is able to coordinate, plan, research, and solve problems for you. This could be deployed on mobile, smart home devices, laptops, or web sites.

Game World NPC's

  • This is a kind of game character that has their own personality, motivations, agenda, and objectives. Furthermore, they would have their own unique memories.
  • This can give NPCs a much more realistic ability to pursue their own objectives, which should make game experiences much more dynamic and unpredictable, thus raising novelty. These can be adapted to 2D or 3D game engines such as PyGame, Unity, or Unreal.

Autonomous Employee

  • This is a version of the ACE that is meant to carry out meaningful and productive work inside a corporation.
  • Whether this is a digital CSR or backoffice worker depends on the deployment.
  • It could also be a "digital team member" that primarily interacts via Discord, Slack, or Microsoft Teams.

Embodied Robot

The ACE Framework is ideal to create self-contained, autonomous machines. Whether they are domestic aid robots or something like WALL-E


Papers


Agent Instructs Large Language Models to be General Zero-Shot Reasoners

We introduce a method to improve the zero-shot reasoning abilities of large language models on general language understanding tasks. Specifically, we build an autonomous agent to instruct the reasoning process of large language models. We show this approach further unleashes the zero-shot reasoning abilities of large language models to more tasks. We study the performance of our method on a wide set of datasets spanning generation, classification, and reasoning. We show that our method generalizes to most tasks and obtains state-of-the-art zero-shot performance on 20 of the 29 datasets that we evaluate. For instance, our method boosts the performance of state-of-the-art large language models by a large margin, including Vicuna-13b (13.3%), Llama-2-70b-chat (23.2%), and GPT-3.5 Turbo (17.0%). Compared to zero-shot chain of thought, our improvement in reasoning is striking, with an average increase of 10.5%. With our method, Llama-2-70b-chat outperforms zero-shot GPT-3.5 Turbo by 10.2%.

DyVal: Graph-informed Dynamic Evaluation of Large Language Models

Large language models (LLMs) have achieved remarkable performance in various evaluation benchmarks. However, concerns about their performance are raised on potential data contamination in their considerable volume of training corpus. Moreover, the static nature and fixed complexity of current benchmarks may inadequately gauge the advancing capabilities of LLMs. In this paper, we introduce DyVal, a novel, general, and flexible evaluation protocol for dynamic evaluation of LLMs. Based on our proposed dynamic evaluation framework, we build graph-informed DyVal by leveraging the structural advantage of directed acyclic graphs to dynamically generate evaluation samples with controllable complexities. DyVal generates challenging evaluation sets on reasoning tasks including mathematics, logical reasoning, and algorithm problems. We evaluate various LLMs ranging from Flan-T5-large to ChatGPT and GPT4. Experiments demonstrate that LLMs perform worse in DyVal-generated evaluation samples with different complexities, emphasizing the significance of dynamic evaluation. We also analyze the failure cases and results of different prompting methods. Moreover, DyVal-generated samples are not only evaluation sets, but also helpful data for fine-tuning to improve the performance of LLMs on existing benchmarks. We hope that DyVal can shed light on the future evaluation research of LLMs.

LoRA ensembles for large language model fine-tuning

Finetuned LLMs often exhibit poor uncertainty quantification, manifesting as overconfidence, poor calibration, and unreliable prediction results on test data or out-of-distribution samples. One approach commonly used in vision for alleviating this issue is a deep ensemble, which constructs an ensemble by training the same model multiple times using different random initializations. However, there is a huge challenge to ensembling LLMs: the most effective LLMs are very, very large. Keeping a single LLM in memory is already challenging enough: keeping an ensemble of e.g. 5 LLMs in memory is impossible in many settings. To address these issues, we propose an ensemble approach using Low-Rank Adapters (LoRA), a parameter-efficient fine-tuning technique. Critically, these low-rank adapters represent a very small number of parameters, orders of magnitude less than the underlying pre-trained model. Thus, it is possible to construct large ensembles of LoRA adapters with almost the same computational overhead as using the original model. We find that LoRA ensembles, applied on its own or on top of pre-existing regularization techniques, gives consistent improvements in predictive accuracy and uncertainty quantification.

There is something to be discovered between LoRA, QLoRA, and ensemble/MoE designs. I am digging into this niche because of an interesting bit I heard from sentdex (if you want to skip to the part I'm talking about, go to 13:58). Around 15:00 minute mark he brings up QLoRA adapters (nothing new) but his approach was interesting.

He eventually shares he is working on a QLoRA ensemble approach with skunkworks (presumably Boeing skunkworks). This confirmed my suspicion. Better yet - he shared his thoughts on how all of this could be done. Watch and support his video for more insights, but the idea boils down to using one model and dynamically swapping the fine-tuned QLoRA adapters. I think this is a highly efficient and unapplied approach. Especially in that MoE and ensemble realm of design. If you're reading this and understood anything I said - get to building! This is a seriously interesting idea that could yield positive results. I will share my findings when I find the time to dig into this more.


Author's Note

This post was authored by the moderator of [email protected] - Blaed. I make games, produce music, write about tech, and develop free open-source artificial intelligence (FOSAI) for fun. I do most of this through a company called HyperionTechnologies a.k.a. HyperTech or HYPERION - a sci-fi company.

Thanks for Reading!

This post was written by a human. For other humans. About machines. Who work for humans for other machines. At least for now... if you found anything about this post interesting, consider subscribing to [email protected] where you can join us on the journey into the great unknown!

Until next time!

Blaed

 

cross-posted from: https://lemmy.world/post/6399678

πŸ€– Happy FOSAI Friday! πŸš€

Friday, October 6, 2023

HyperTech News Report #0003

Hello Everyone!

This week highlights a wave of new papers and frameworks that expand upon LLM functionalities. With a tsunami of applications on the horizon I foresee a bedrock of tools to preceed. I'm not sure what kits and processes will end up part of this bedrock, but I hope some of these methods end up interesting or helpful to your workflow!

Table of Contents

Community Changelog

Image of the Week

This image of the week comes from one of my own projects! I hope you don't mind me sharing.. I was really happy with this result. This was generated from an SDXL model I trained and host on Replicate. I use an mock ensemble approach to generate various game assets for an experimental roguelike I'm making with a colleague.

My current method is not at all efficient, but I have fun. Right now, I have three SDXL models I interact with, each generating art I can use for my project. Andraxus takes care of wallpapers and in-game levels (this image you're seeing here), his in-game companion Biazera imagines characters and entities of this world, while Cerephelo tinkers and toils over the machinations within - crafting items, loot, powerups, etc.

I've been hesitant self-promoting here. But if there's genuine interest in this project I would be more than happy sharing more details. It's still in pre-alpha development, but there were plans releasing all of the models we use as open-source (obviously). We're still working on the engine though. Let me know if you want to see more on this project.


News


  1. Arxiv Publications Workflow: A new workflow has been introduced that allows users to scrape search topics from Arxiv, converting the results into markdown (MD) format. This makes it easier to digest and understand topics from Arxiv published content. The tool, available on GitHub, is particularly useful for those who wish to delve deeper into research papers and run their own research processes.

  2. Texting LLMs from Your Phone: A guide has been shared that enables users to communicate with their personal assistants via simple text messages. The process involves setting up a Twilio account, purchasing and registering a phone number, and then integrating it with the Replicate platform. The code, available on GitHub, makes it possible to send and receive messages from LLMs directly on one's phone.

  3. Microsoft's AutoGen: Microsoft has released AutoGen, a tool designed to aid in the creation of autonomous LLM agents. Compatible with ChatGPT models, AutoGen facilitates the development of LLM applications using multiple agents that can converse with each other to solve tasks. The framework is customizable and allows for seamless human participation. More details can be found on GitHub.

  4. Promptbench and ACE Framework: Promptbench is a new project focused on the evaluation and benchmarking of models. Stemming from the DyVal paper, it aims to provide reliable insights into model performance. On the other hand, the ACE Framework, designed for autonomous cognitive entities, offers a unique approach to agent tooling. While still in its early stages, it promises to bring about innovative implementations in the realms of personal assistants, game world NPCs, autonomous employees, and embodied robots.

  5. Research Highlights: Several papers have been published that delve into the intricacies of LLMs. One paper introduces a method to enhance the zero-shot reasoning abilities of LLMs, while another, titled DyVal, proposes a dynamic evaluation protocol for LLMs. Additionally, the concept of Low-Rank Adapters (LoRA) ensembles for LLM fine-tuning has been explored, emphasizing the potential of using one model and dynamically swapping the fine-tuned QLoRA adapters.


Tools & Frameworks


Keep Up w/ Arxiv Publications

Due to a drastic change in personal and work schedules, I've had to shift how I research and develop posts and projects for you guys. That being said, I found this workflow from the same author of the ACE Framework particularly helpful. It scrapes a search topic from Arxiv and returns a massive XML that is converted to markdown (MD) to then be used as an injectable context report for a LLM of your choosing (to further break down and understand topics) or as a well of information for the classic CTRL + F search. But at this point, info is aggregated (and human readable) from Arxiv published content.

After reading abstractions you can further drill into each paper and dissect / run your own research processes as you see fit. There is definitely more room for automation and organization here I'm sure, but this has been a big resource for me lately so I wanted to proliferate it for others who might find it helpful too.

Text LLMs from Your Phone

I had an itch to make my personal assistants more accessible - so I started investigating ways I could simply text them from my iPhone (via simple sms). There are many other ways I could've done this, but texting has been something I always like to default to in communications. So, I found this cool guide that uses infra I already prefer (Replicate) and has a bonus LangChain integration - which opens up the door to a ton of other opportunities down the line.

This tutorial was pretty straightforward - but to be honest, making the Twilio account, buying a phone number (then registering it) took the longest. The code itself takes less than 10 minutes to get up and running with ngrok. Super simple and straightforward there. The Twilio process? Not so much.. but it was worth the pain!

I am still waiting on my phone number to be verified (so that the Replicate inference endpoint can actually send SMS back to me) but I ended the night successfully texting the server on my local PC. It was wild texting the Ahsoka example from my phone and seeing the POST response return (even though it didn't go through SMS I could still see the server successfully receive my incoming message/prompt). I think there's a lot of fun to be had giving casual phone numbers and personalities to assistants like this. Especially if you want to LangChain some functions beyond just the conversation. If there's more interest on this topic, I can share how my assistant evolves once it gets full access to return SMS. I am designing this to streamline my personal life, and if it proves to be useful I will absolutely release the project as open-source.

AutoGen

With Agents on the rise, tools and automation pipelines to build them have become increasingly more important to consider. It seems like Microsoft is well aware of this, and thus released AutoGen, a tool to help enable this automation tooling and creation of autonomous LLM agents. AutoGen is compatible with ChatGPT models and is being kitted for local LLMs as we speak.

AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.

Promptbench

I recently found promptbench - a project that seems to have stemmed from the DyVal paper (shared below). I for one appreciate some of the new tools that are releasing focused around the evaluation and benchmarking of models. I hope we continue to see more evals, benchmarks, and projects that return us insights we can rely upon.

ACE Framework

A new framework has been proposed and designed for autonomous cognitive entities. This appears similar to agents and their style of tooling, but with a different architecture approach? I don't believe implementation of this is ready, but it may be soon and something to keep an eye on.

There are many possible implementations of the ACE Framework. Rather than detail every possible permutation, here is a list of categories that we perceive as likely and viable.

Personal Assistant and/or Companion

  • This is a self-contained version of ACE that is intended to interact with one user.
  • Think of Cortana from HALO, Samantha from HER, or Joi from Blade Runner 2049. (yes, we recognize these are all sexualized female avatars)
  • The idea would be to create something that is effectively a personal Executive Assistant that is able to coordinate, plan, research, and solve problems for you. This could be deployed on mobile, smart home devices, laptops, or web sites.

Game World NPC's

  • This is a kind of game character that has their own personality, motivations, agenda, and objectives. Furthermore, they would have their own unique memories.
  • This can give NPCs a much more realistic ability to pursue their own objectives, which should make game experiences much more dynamic and unpredictable, thus raising novelty. These can be adapted to 2D or 3D game engines such as PyGame, Unity, or Unreal.

Autonomous Employee

  • This is a version of the ACE that is meant to carry out meaningful and productive work inside a corporation.
  • Whether this is a digital CSR or backoffice worker depends on the deployment.
  • It could also be a "digital team member" that primarily interacts via Discord, Slack, or Microsoft Teams.

Embodied Robot

The ACE Framework is ideal to create self-contained, autonomous machines. Whether they are domestic aid robots or something like WALL-E


Papers


Agent Instructs Large Language Models to be General Zero-Shot Reasoners

We introduce a method to improve the zero-shot reasoning abilities of large language models on general language understanding tasks. Specifically, we build an autonomous agent to instruct the reasoning process of large language models. We show this approach further unleashes the zero-shot reasoning abilities of large language models to more tasks. We study the performance of our method on a wide set of datasets spanning generation, classification, and reasoning. We show that our method generalizes to most tasks and obtains state-of-the-art zero-shot performance on 20 of the 29 datasets that we evaluate. For instance, our method boosts the performance of state-of-the-art large language models by a large margin, including Vicuna-13b (13.3%), Llama-2-70b-chat (23.2%), and GPT-3.5 Turbo (17.0%). Compared to zero-shot chain of thought, our improvement in reasoning is striking, with an average increase of 10.5%. With our method, Llama-2-70b-chat outperforms zero-shot GPT-3.5 Turbo by 10.2%.

DyVal: Graph-informed Dynamic Evaluation of Large Language Models

Large language models (LLMs) have achieved remarkable performance in various evaluation benchmarks. However, concerns about their performance are raised on potential data contamination in their considerable volume of training corpus. Moreover, the static nature and fixed complexity of current benchmarks may inadequately gauge the advancing capabilities of LLMs. In this paper, we introduce DyVal, a novel, general, and flexible evaluation protocol for dynamic evaluation of LLMs. Based on our proposed dynamic evaluation framework, we build graph-informed DyVal by leveraging the structural advantage of directed acyclic graphs to dynamically generate evaluation samples with controllable complexities. DyVal generates challenging evaluation sets on reasoning tasks including mathematics, logical reasoning, and algorithm problems. We evaluate various LLMs ranging from Flan-T5-large to ChatGPT and GPT4. Experiments demonstrate that LLMs perform worse in DyVal-generated evaluation samples with different complexities, emphasizing the significance of dynamic evaluation. We also analyze the failure cases and results of different prompting methods. Moreover, DyVal-generated samples are not only evaluation sets, but also helpful data for fine-tuning to improve the performance of LLMs on existing benchmarks. We hope that DyVal can shed light on the future evaluation research of LLMs.

LoRA ensembles for large language model fine-tuning

Finetuned LLMs often exhibit poor uncertainty quantification, manifesting as overconfidence, poor calibration, and unreliable prediction results on test data or out-of-distribution samples. One approach commonly used in vision for alleviating this issue is a deep ensemble, which constructs an ensemble by training the same model multiple times using different random initializations. However, there is a huge challenge to ensembling LLMs: the most effective LLMs are very, very large. Keeping a single LLM in memory is already challenging enough: keeping an ensemble of e.g. 5 LLMs in memory is impossible in many settings. To address these issues, we propose an ensemble approach using Low-Rank Adapters (LoRA), a parameter-efficient fine-tuning technique. Critically, these low-rank adapters represent a very small number of parameters, orders of magnitude less than the underlying pre-trained model. Thus, it is possible to construct large ensembles of LoRA adapters with almost the same computational overhead as using the original model. We find that LoRA ensembles, applied on its own or on top of pre-existing regularization techniques, gives consistent improvements in predictive accuracy and uncertainty quantification.

There is something to be discovered between LoRA, QLoRA, and ensemble/MoE designs. I am digging into this niche because of an interesting bit I heard from sentdex (if you want to skip to the part I'm talking about, go to 13:58). Around 15:00 minute mark he brings up QLoRA adapters (nothing new) but his approach was interesting.

He eventually shares he is working on a QLoRA ensemble approach with skunkworks (presumably Boeing skunkworks). This confirmed my suspicion. Better yet - he shared his thoughts on how all of this could be done. Watch and support his video for more insights, but the idea boils down to using one model and dynamically swapping the fine-tuned QLoRA adapters. I think this is a highly efficient and unapplied approach. Especially in that MoE and ensemble realm of design. If you're reading this and understood anything I said - get to building! This is a seriously interesting idea that could yield positive results. I will share my findings when I find the time to dig into this more.


Author's Note

This post was authored by the moderator of [email protected] - Blaed. I make games, produce music, write about tech, and develop free open-source artificial intelligence (FOSAI) for fun. I do most of this through a company called HyperionTechnologies a.k.a. HyperTech or HYPERION - a sci-fi company.

Thanks for Reading!

This post was written by a human. For other humans. About machines. Who work for humans for other machines. At least for now... if you found anything about this post interesting, consider subscribing to [email protected] where you can join us on the journey into the great unknown!

Until next time!

Blaed

 

cross-posted from: https://lemmy.world/post/6399678

πŸ€– Happy FOSAI Friday! πŸš€

Friday, October 6, 2023

HyperTech News Report #0003

Hello Everyone!

This week highlights a wave of new papers and frameworks that expand upon LLM functionalities. With a tsunami of applications on the horizon I foresee a bedrock of tools to preceed. I'm not sure what kits and processes will end up part of this bedrock, but I hope some of these methods end up interesting or helpful to your workflow!

Table of Contents

Community Changelog

Image of the Week

This image of the week comes from one of my own projects! I hope you don't mind me sharing.. I was really happy with this result. This was generated from an SDXL model I trained and host on Replicate. I use an mock ensemble approach to generate various game assets for an experimental roguelike I'm making with a colleague.

My current method is not at all efficient, but I have fun. Right now, I have three SDXL models I interact with, each generating art I can use for my project. Andraxus takes care of wallpapers and in-game levels (this image you're seeing here), his in-game companion Biazera imagines characters and entities of this world, while Cerephelo tinkers and toils over the machinations within - crafting items, loot, powerups, etc.

I've been hesitant self-promoting here. But if there's genuine interest in this project I would be more than happy sharing more details. It's still in pre-alpha development, but there were plans releasing all of the models we use as open-source (obviously). We're still working on the engine though. Let me know if you want to see more on this project.


News


  1. Arxiv Publications Workflow: A new workflow has been introduced that allows users to scrape search topics from Arxiv, converting the results into markdown (MD) format. This makes it easier to digest and understand topics from Arxiv published content. The tool, available on GitHub, is particularly useful for those who wish to delve deeper into research papers and run their own research processes.

  2. Texting LLMs from Your Phone: A guide has been shared that enables users to communicate with their personal assistants via simple text messages. The process involves setting up a Twilio account, purchasing and registering a phone number, and then integrating it with the Replicate platform. The code, available on GitHub, makes it possible to send and receive messages from LLMs directly on one's phone.

  3. Microsoft's AutoGen: Microsoft has released AutoGen, a tool designed to aid in the creation of autonomous LLM agents. Compatible with ChatGPT models, AutoGen facilitates the development of LLM applications using multiple agents that can converse with each other to solve tasks. The framework is customizable and allows for seamless human participation. More details can be found on GitHub.

  4. Promptbench and ACE Framework: Promptbench is a new project focused on the evaluation and benchmarking of models. Stemming from the DyVal paper, it aims to provide reliable insights into model performance. On the other hand, the ACE Framework, designed for autonomous cognitive entities, offers a unique approach to agent tooling. While still in its early stages, it promises to bring about innovative implementations in the realms of personal assistants, game world NPCs, autonomous employees, and embodied robots.

  5. Research Highlights: Several papers have been published that delve into the intricacies of LLMs. One paper introduces a method to enhance the zero-shot reasoning abilities of LLMs, while another, titled DyVal, proposes a dynamic evaluation protocol for LLMs. Additionally, the concept of Low-Rank Adapters (LoRA) ensembles for LLM fine-tuning has been explored, emphasizing the potential of using one model and dynamically swapping the fine-tuned QLoRA adapters.


Tools & Frameworks


Keep Up w/ Arxiv Publications

Due to a drastic change in personal and work schedules, I've had to shift how I research and develop posts and projects for you guys. That being said, I found this workflow from the same author of the ACE Framework particularly helpful. It scrapes a search topic from Arxiv and returns a massive XML that is converted to markdown (MD) to then be used as an injectable context report for a LLM of your choosing (to further break down and understand topics) or as a well of information for the classic CTRL + F search. But at this point, info is aggregated (and human readable) from Arxiv published content.

After reading abstractions you can further drill into each paper and dissect / run your own research processes as you see fit. There is definitely more room for automation and organization here I'm sure, but this has been a big resource for me lately so I wanted to proliferate it for others who might find it helpful too.

Text LLMs from Your Phone

I had an itch to make my personal assistants more accessible - so I started investigating ways I could simply text them from my iPhone (via simple sms). There are many other ways I could've done this, but texting has been something I always like to default to in communications. So, I found this cool guide that uses infra I already prefer (Replicate) and has a bonus LangChain integration - which opens up the door to a ton of other opportunities down the line.

This tutorial was pretty straightforward - but to be honest, making the Twilio account, buying a phone number (then registering it) took the longest. The code itself takes less than 10 minutes to get up and running with ngrok. Super simple and straightforward there. The Twilio process? Not so much.. but it was worth the pain!

I am still waiting on my phone number to be verified (so that the Replicate inference endpoint can actually send SMS back to me) but I ended the night successfully texting the server on my local PC. It was wild texting the Ahsoka example from my phone and seeing the POST response return (even though it didn't go through SMS I could still see the server successfully receive my incoming message/prompt). I think there's a lot of fun to be had giving casual phone numbers and personalities to assistants like this. Especially if you want to LangChain some functions beyond just the conversation. If there's more interest on this topic, I can share how my assistant evolves once it gets full access to return SMS. I am designing this to streamline my personal life, and if it proves to be useful I will absolutely release the project as open-source.

AutoGen

With Agents on the rise, tools and automation pipelines to build them have become increasingly more important to consider. It seems like Microsoft is well aware of this, and thus released AutoGen, a tool to help enable this automation tooling and creation of autonomous LLM agents. AutoGen is compatible with ChatGPT models and is being kitted for local LLMs as we speak.

AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.

Promptbench

I recently found promptbench - a project that seems to have stemmed from the DyVal paper (shared below). I for one appreciate some of the new tools that are releasing focused around the evaluation and benchmarking of models. I hope we continue to see more evals, benchmarks, and projects that return us insights we can rely upon.

ACE Framework

A new framework has been proposed and designed for autonomous cognitive entities. This appears similar to agents and their style of tooling, but with a different architecture approach? I don't believe implementation of this is ready, but it may be soon and something to keep an eye on.

There are many possible implementations of the ACE Framework. Rather than detail every possible permutation, here is a list of categories that we perceive as likely and viable.

Personal Assistant and/or Companion

  • This is a self-contained version of ACE that is intended to interact with one user.
  • Think of Cortana from HALO, Samantha from HER, or Joi from Blade Runner 2049. (yes, we recognize these are all sexualized female avatars)
  • The idea would be to create something that is effectively a personal Executive Assistant that is able to coordinate, plan, research, and solve problems for you. This could be deployed on mobile, smart home devices, laptops, or web sites.

Game World NPC's

  • This is a kind of game character that has their own personality, motivations, agenda, and objectives. Furthermore, they would have their own unique memories.
  • This can give NPCs a much more realistic ability to pursue their own objectives, which should make game experiences much more dynamic and unpredictable, thus raising novelty. These can be adapted to 2D or 3D game engines such as PyGame, Unity, or Unreal.

Autonomous Employee

  • This is a version of the ACE that is meant to carry out meaningful and productive work inside a corporation.
  • Whether this is a digital CSR or backoffice worker depends on the deployment.
  • It could also be a "digital team member" that primarily interacts via Discord, Slack, or Microsoft Teams.

Embodied Robot

The ACE Framework is ideal to create self-contained, autonomous machines. Whether they are domestic aid robots or something like WALL-E


Papers


Agent Instructs Large Language Models to be General Zero-Shot Reasoners

We introduce a method to improve the zero-shot reasoning abilities of large language models on general language understanding tasks. Specifically, we build an autonomous agent to instruct the reasoning process of large language models. We show this approach further unleashes the zero-shot reasoning abilities of large language models to more tasks. We study the performance of our method on a wide set of datasets spanning generation, classification, and reasoning. We show that our method generalizes to most tasks and obtains state-of-the-art zero-shot performance on 20 of the 29 datasets that we evaluate. For instance, our method boosts the performance of state-of-the-art large language models by a large margin, including Vicuna-13b (13.3%), Llama-2-70b-chat (23.2%), and GPT-3.5 Turbo (17.0%). Compared to zero-shot chain of thought, our improvement in reasoning is striking, with an average increase of 10.5%. With our method, Llama-2-70b-chat outperforms zero-shot GPT-3.5 Turbo by 10.2%.

DyVal: Graph-informed Dynamic Evaluation of Large Language Models

Large language models (LLMs) have achieved remarkable performance in various evaluation benchmarks. However, concerns about their performance are raised on potential data contamination in their considerable volume of training corpus. Moreover, the static nature and fixed complexity of current benchmarks may inadequately gauge the advancing capabilities of LLMs. In this paper, we introduce DyVal, a novel, general, and flexible evaluation protocol for dynamic evaluation of LLMs. Based on our proposed dynamic evaluation framework, we build graph-informed DyVal by leveraging the structural advantage of directed acyclic graphs to dynamically generate evaluation samples with controllable complexities. DyVal generates challenging evaluation sets on reasoning tasks including mathematics, logical reasoning, and algorithm problems. We evaluate various LLMs ranging from Flan-T5-large to ChatGPT and GPT4. Experiments demonstrate that LLMs perform worse in DyVal-generated evaluation samples with different complexities, emphasizing the significance of dynamic evaluation. We also analyze the failure cases and results of different prompting methods. Moreover, DyVal-generated samples are not only evaluation sets, but also helpful data for fine-tuning to improve the performance of LLMs on existing benchmarks. We hope that DyVal can shed light on the future evaluation research of LLMs.

LoRA ensembles for large language model fine-tuning

Finetuned LLMs often exhibit poor uncertainty quantification, manifesting as overconfidence, poor calibration, and unreliable prediction results on test data or out-of-distribution samples. One approach commonly used in vision for alleviating this issue is a deep ensemble, which constructs an ensemble by training the same model multiple times using different random initializations. However, there is a huge challenge to ensembling LLMs: the most effective LLMs are very, very large. Keeping a single LLM in memory is already challenging enough: keeping an ensemble of e.g. 5 LLMs in memory is impossible in many settings. To address these issues, we propose an ensemble approach using Low-Rank Adapters (LoRA), a parameter-efficient fine-tuning technique. Critically, these low-rank adapters represent a very small number of parameters, orders of magnitude less than the underlying pre-trained model. Thus, it is possible to construct large ensembles of LoRA adapters with almost the same computational overhead as using the original model. We find that LoRA ensembles, applied on its own or on top of pre-existing regularization techniques, gives consistent improvements in predictive accuracy and uncertainty quantification.

There is something to be discovered between LoRA, QLoRA, and ensemble/MoE designs. I am digging into this niche because of an interesting bit I heard from sentdex (if you want to skip to the part I'm talking about, go to 13:58). Around 15:00 minute mark he brings up QLoRA adapters (nothing new) but his approach was interesting.

He eventually shares he is working on a QLoRA ensemble approach with skunkworks (presumably Boeing skunkworks). This confirmed my suspicion. Better yet - he shared his thoughts on how all of this could be done. Watch and support his video for more insights, but the idea boils down to using one model and dynamically swapping the fine-tuned QLoRA adapters. I think this is a highly efficient and unapplied approach. Especially in that MoE and ensemble realm of design. If you're reading this and understood anything I said - get to building! This is a seriously interesting idea that could yield positive results. I will share my findings when I find the time to dig into this more.


Author's Note

This post was authored by the moderator of [email protected] - Blaed. I make games, produce music, write about tech, and develop free open-source artificial intelligence (FOSAI) for fun. I do most of this through a company called HyperionTechnologies a.k.a. HyperTech or HYPERION - a sci-fi company.

Thanks for Reading!

This post was written by a human. For other humans. About machines. Who work for humans for other machines. At least for now... if you found anything about this post interesting, consider subscribing to [email protected] where you can join us on the journey into the great unknown!

Until next time!

Blaed

5
submitted 1 year ago* (last edited 1 year ago) by Blaed to c/fosai
 

πŸ€– Happy FOSAI Friday! πŸš€

Friday, October 6, 2023

HyperTech News Report #0003

Hello Everyone!

This week highlights a wave of new papers and frameworks that expand upon LLM functionalities. With a tsunami of applications on the horizon I foresee a bedrock of tools to preceed. I'm not sure what kits and processes will end up part of this bedrock, but I hope some of these methods end up interesting or helpful to your workflow!

Table of Contents

Community Changelog

Image of the Week

This image of the week comes from one of my own projects! I hope you don't mind me sharing.. I was really happy with this result. This was generated from an SDXL model I trained and host on Replicate. I use an mock ensemble approach to generate various game assets for an experimental roguelike I'm making with a colleague.

My current method is not at all efficient, but I have fun. Right now, I have three SDXL models I interact with, each generating art I can use for my project. Andraxus takes care of wallpapers and in-game levels (this image you're seeing here), his in-game companion Biazera imagines characters and entities of this world, while Cerephelo tinkers and toils over the machinations within - crafting items, loot, powerups, etc.

I've been hesitant self-promoting here. But if there's genuine interest in this project I would be more than happy sharing more details. It's still in pre-alpha development, but there were plans releasing all of the models we use as open-source (obviously). We're still working on the engine though. Let me know if you want to see more on this project.


News


  1. Arxiv Publications Workflow: A new workflow has been introduced that allows users to scrape search topics from Arxiv, converting the results into markdown (MD) format. This makes it easier to digest and understand topics from Arxiv published content. The tool, available on GitHub, is particularly useful for those who wish to delve deeper into research papers and run their own research processes.

  2. Texting LLMs from Your Phone: A guide has been shared that enables users to communicate with their personal assistants via simple text messages. The process involves setting up a Twilio account, purchasing and registering a phone number, and then integrating it with the Replicate platform. The code, available on GitHub, makes it possible to send and receive messages from LLMs directly on one's phone.

  3. Microsoft's AutoGen: Microsoft has released AutoGen, a tool designed to aid in the creation of autonomous LLM agents. Compatible with ChatGPT models, AutoGen facilitates the development of LLM applications using multiple agents that can converse with each other to solve tasks. The framework is customizable and allows for seamless human participation. More details can be found on GitHub.

  4. Promptbench and ACE Framework: Promptbench is a new project focused on the evaluation and benchmarking of models. Stemming from the DyVal paper, it aims to provide reliable insights into model performance. On the other hand, the ACE Framework, designed for autonomous cognitive entities, offers a unique approach to agent tooling. While still in its early stages, it promises to bring about innovative implementations in the realms of personal assistants, game world NPCs, autonomous employees, and embodied robots.

  5. Research Highlights: Several papers have been published that delve into the intricacies of LLMs. One paper introduces a method to enhance the zero-shot reasoning abilities of LLMs, while another, titled DyVal, proposes a dynamic evaluation protocol for LLMs. Additionally, the concept of Low-Rank Adapters (LoRA) ensembles for LLM fine-tuning has been explored, emphasizing the potential of using one model and dynamically swapping the fine-tuned QLoRA adapters.


Tools & Frameworks


Keep Up w/ Arxiv Publications

Due to a drastic change in personal and work schedules, I've had to shift how I research and develop posts and projects for you guys. That being said, I found this workflow from the same author of the ACE Framework particularly helpful. It scrapes a search topic from Arxiv and returns a massive XML that is converted to markdown (MD) to then be used as an injectable context report for a LLM of your choosing (to further break down and understand topics) or as a well of information for the classic CTRL + F search. But at this point, info is aggregated (and human readable) from Arxiv published content.

After reading abstractions you can further drill into each paper and dissect / run your own research processes as you see fit. There is definitely more room for automation and organization here I'm sure, but this has been a big resource for me lately so I wanted to proliferate it for others who might find it helpful too.

Text LLMs from Your Phone

I had an itch to make my personal assistants more accessible - so I started investigating ways I could simply text them from my iPhone (via simple sms). There are many other ways I could've done this, but texting has been something I always like to default to in communications. So, I found this cool guide that uses infra I already prefer (Replicate) and has a bonus LangChain integration - which opens up the door to a ton of other opportunities down the line.

This tutorial was pretty straightforward - but to be honest, making the Twilio account, buying a phone number (then registering it) took the longest. The code itself takes less than 10 minutes to get up and running with ngrok. Super simple and straightforward there. The Twilio process? Not so much.. but it was worth the pain!

I am still waiting on my phone number to be verified (so that the Replicate inference endpoint can actually send SMS back to me) but I ended the night successfully texting the server on my local PC. It was wild texting the Ahsoka example from my phone and seeing the POST response return (even though it didn't go through SMS I could still see the server successfully receive my incoming message/prompt). I think there's a lot of fun to be had giving casual phone numbers and personalities to assistants like this. Especially if you want to LangChain some functions beyond just the conversation. If there's more interest on this topic, I can share how my assistant evolves once it gets full access to return SMS. I am designing this to streamline my personal life, and if it proves to be useful I will absolutely release the project as open-source.

AutoGen

With Agents on the rise, tools and automation pipelines to build them have become increasingly more important to consider. It seems like Microsoft is well aware of this, and thus released AutoGen, a tool to help enable this automation tooling and creation of autonomous LLM agents. AutoGen is compatible with ChatGPT models and is being kitted for local LLMs as we speak.

AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.

Promptbench

I recently found promptbench - a project that seems to have stemmed from the DyVal paper (shared below). I for one appreciate some of the new tools that are releasing focused around the evaluation and benchmarking of models. I hope we continue to see more evals, benchmarks, and projects that return us insights we can rely upon.

ACE Framework

A new framework has been proposed and designed for autonomous cognitive entities. This appears similar to agents and their style of tooling, but with a different architecture approach? I don't believe implementation of this is ready, but it may be soon and something to keep an eye on.

There are many possible implementations of the ACE Framework. Rather than detail every possible permutation, here is a list of categories that we perceive as likely and viable.

Personal Assistant and/or Companion

  • This is a self-contained version of ACE that is intended to interact with one user.
  • Think of Cortana from HALO, Samantha from HER, or Joi from Blade Runner 2049. (yes, we recognize these are all sexualized female avatars)
  • The idea would be to create something that is effectively a personal Executive Assistant that is able to coordinate, plan, research, and solve problems for you. This could be deployed on mobile, smart home devices, laptops, or web sites.

Game World NPC's

  • This is a kind of game character that has their own personality, motivations, agenda, and objectives. Furthermore, they would have their own unique memories.
  • This can give NPCs a much more realistic ability to pursue their own objectives, which should make game experiences much more dynamic and unpredictable, thus raising novelty. These can be adapted to 2D or 3D game engines such as PyGame, Unity, or Unreal.

Autonomous Employee

  • This is a version of the ACE that is meant to carry out meaningful and productive work inside a corporation.
  • Whether this is a digital CSR or backoffice worker depends on the deployment.
  • It could also be a "digital team member" that primarily interacts via Discord, Slack, or Microsoft Teams.

Embodied Robot

The ACE Framework is ideal to create self-contained, autonomous machines. Whether they are domestic aid robots or something like WALL-E


Papers


Agent Instructs Large Language Models to be General Zero-Shot Reasoners

We introduce a method to improve the zero-shot reasoning abilities of large language models on general language understanding tasks. Specifically, we build an autonomous agent to instruct the reasoning process of large language models. We show this approach further unleashes the zero-shot reasoning abilities of large language models to more tasks. We study the performance of our method on a wide set of datasets spanning generation, classification, and reasoning. We show that our method generalizes to most tasks and obtains state-of-the-art zero-shot performance on 20 of the 29 datasets that we evaluate. For instance, our method boosts the performance of state-of-the-art large language models by a large margin, including Vicuna-13b (13.3%), Llama-2-70b-chat (23.2%), and GPT-3.5 Turbo (17.0%). Compared to zero-shot chain of thought, our improvement in reasoning is striking, with an average increase of 10.5%. With our method, Llama-2-70b-chat outperforms zero-shot GPT-3.5 Turbo by 10.2%.

DyVal: Graph-informed Dynamic Evaluation of Large Language Models

Large language models (LLMs) have achieved remarkable performance in various evaluation benchmarks. However, concerns about their performance are raised on potential data contamination in their considerable volume of training corpus. Moreover, the static nature and fixed complexity of current benchmarks may inadequately gauge the advancing capabilities of LLMs. In this paper, we introduce DyVal, a novel, general, and flexible evaluation protocol for dynamic evaluation of LLMs. Based on our proposed dynamic evaluation framework, we build graph-informed DyVal by leveraging the structural advantage of directed acyclic graphs to dynamically generate evaluation samples with controllable complexities. DyVal generates challenging evaluation sets on reasoning tasks including mathematics, logical reasoning, and algorithm problems. We evaluate various LLMs ranging from Flan-T5-large to ChatGPT and GPT4. Experiments demonstrate that LLMs perform worse in DyVal-generated evaluation samples with different complexities, emphasizing the significance of dynamic evaluation. We also analyze the failure cases and results of different prompting methods. Moreover, DyVal-generated samples are not only evaluation sets, but also helpful data for fine-tuning to improve the performance of LLMs on existing benchmarks. We hope that DyVal can shed light on the future evaluation research of LLMs.

LoRA ensembles for large language model fine-tuning

Finetuned LLMs often exhibit poor uncertainty quantification, manifesting as overconfidence, poor calibration, and unreliable prediction results on test data or out-of-distribution samples. One approach commonly used in vision for alleviating this issue is a deep ensemble, which constructs an ensemble by training the same model multiple times using different random initializations. However, there is a huge challenge to ensembling LLMs: the most effective LLMs are very, very large. Keeping a single LLM in memory is already challenging enough: keeping an ensemble of e.g. 5 LLMs in memory is impossible in many settings. To address these issues, we propose an ensemble approach using Low-Rank Adapters (LoRA), a parameter-efficient fine-tuning technique. Critically, these low-rank adapters represent a very small number of parameters, orders of magnitude less than the underlying pre-trained model. Thus, it is possible to construct large ensembles of LoRA adapters with almost the same computational overhead as using the original model. We find that LoRA ensembles, applied on its own or on top of pre-existing regularization techniques, gives consistent improvements in predictive accuracy and uncertainty quantification.

There is something to be discovered between LoRA, QLoRA, and ensemble/MoE designs. I am digging into this niche because of an interesting bit I heard from sentdex (if you want to skip to the part I'm talking about, go to 13:58). Around 15:00 minute mark he brings up QLoRA adapters (nothing new) but his approach was interesting.

He eventually shares he is working on a QLoRA ensemble approach with skunkworks (presumably Boeing skunkworks). This confirmed my suspicion. Better yet - he shared his thoughts on how all of this could be done. Watch and support his video for more insights, but the idea boils down to using one model and dynamically swapping the fine-tuned QLoRA adapters. I think this is a highly efficient and unapplied approach. Especially in that MoE and ensemble realm of design. If you're reading this and understood anything I said - get to building! This is a seriously interesting idea that could yield positive results. I will share my findings when I find the time to dig into this more.


Author's Note

This post was authored by the moderator of [email protected] - Blaed. I make games, produce music, write about tech, and develop free open-source artificial intelligence (FOSAI) for fun. I do most of this through a company called HyperionTechnologies a.k.a. HyperTech or HYPERION - a sci-fi company.

Thanks for Reading!

This post was written by a human. For other humans. About machines. Who work for humans for other machines. At least for now...

Until next time!

Blaed

11
submitted 1 year ago* (last edited 1 year ago) by Blaed to c/fosai
 

What have been your experiences with it? What does your workflow look like?

Curious to hear preferred models and pipelines!

Frameworks

Databases

Tutorials

Am I missing any other RAG resources?

27
submitted 1 year ago* (last edited 1 year ago) by Blaed to c/fosai
 

Starting a Mistral Megathread to aggregate resources.

This is my new favorite 7B model. It is really good for what it is. I am excited to see what we can tune together. I will be using this thread as a living document, expect a lot of changes and notes, revisions and updates.

Let me know if there's something in particular you want to see here. I will be adding to this thread throughout my fine-tuning journey with Mistral.

Mistral Model Megathread


Key

  • Link #1 - Base Model
  • Link #2 - Instruct Model

Quantized Base Models from TheBloke

GPTQ

GGUF

AWQ


Quantized Samantha Models from TheBloke

GPTQ

GGUF

AWQ


Quantized Kimiko Models from TheBloke

GPTQ

GGUF

AWQ


Quantized Dolphin Models from TheBloke

GPTQ

GGUF

AWQ


Quantized Orca Models from TheBloke

GPTQ

GGUF

AWQ


Quantized Airoboros Models from TheBloke

GPTQ

GGUF

AWQ


If you like to run any of the quantized/optimized models from TheBloke, do visit the full model pages from each of the quantized model cards to see and support the developers of each fine-tuned model.

11
submitted 1 year ago* (last edited 1 year ago) by Blaed to c/fosai
 

Today I am very excited to share with you AutoGen - a new framework for enabling next generation LLM applications.

This new process published by Microsoft Research Blog details a method on how to easily and efficiently deploy agentic LLMs across your workflows.

AutoGen

It requires a lot of effort and expertise to design, implement, and optimize a workflow that can leverage the full potential of large language models (LLMs). Automating these workflows has tremendous value. As developers begin to create increasingly complex LLM-based applications, workflows will inevitably grow more intricate. The potential design space for such workflows could be vast and complex, thereby heightening the challenge of orchestrating an optimal workflow with robust performance.

AutoGen is a framework for simplifying the orchestration, optimization, and automation of LLM workflows. It offers customizable and conversable agents that leverage the strongest capabilities of the most advanced LLMs, like GPT-4, while addressing their limitations by integrating with humans and tools and having conversations between multiple agents via automated chat.

With AutoGen, building a complex multi-agent conversation system boils down to:

  • Defining a set of agents with specialized capabilities and roles.
  • Defining the interaction behavior between agents, i.e., what to reply when an agent receives messages from another agent.

Both steps are intuitive and modular, making these agents reusable and composable. For example, to build a system for code-based question answering, one can design the agents and their interactions as in Figure 2. Such a system is shown to reduce the number of manual interactions needed from 3x to 10x in applications like supply-chain optimization(opens in new tab). Using AutoGen leads to more than a 4x reduction in coding effort.

The agent conversation-centric design has numerous benefits, including that it:

  • Naturally handles ambiguity, feedback, progress, and collaboration. Enables effective coding-related tasks, like tool use with back-and-forth troubleshooting.
  • Allows users to seamlessly opt in or opt out via an agent in the chat.
  • Achieves a collective goal with the cooperation of multiple specialists.


Getting Started

AutoGen (in preview) is freely available as a Python package. To install it, run

pip install pyautogen

You can quickly enable a powerful experience with just a few lines of code:

import autogen

assistant = autogen.AssistantAgent("assistant")

user_proxy = autogen.UserProxyAgent("user_proxy")

user_proxy.initiate_chat(assistant, message="Show me the YTD gain of 10 largest technology companies as of today.")

# This triggers automated chat to solve the task

Check examples for a wide variety of tasks: https://microsoft.github.io/autogen/docs/Examples/AutoGen-AgentChat


Learn More

I feel like I've been mentioning this a lot lately, but agentic LLMs and emergent AI tooling frameworks like these are what will to return us the most value. If you're looking to expand your horizons beyond just chatting with LLMs, integrating agentic tools is an interesting topic to explore. There is much to be built in this space of exciting AI!

 

To me, it's pretty obvious how AGI can change the world.

I'm curious to hear everyone else's thoughts on this.

[–] Blaed 2 points 1 year ago* (last edited 1 year ago)

I could not agree more. I really enjoy Andrej Karpathy’s model where in the future AGI does 99% of the technical work and the human in the loop does the creative and critical 1%.

16
submitted 1 year ago by Blaed to c/fosai
 

Genuinely curious.

Why do you like LLMs? What hopes do you have for AI & AGI in our near and distant future?

[–] Blaed 6 points 1 year ago (1 children)

Mistral seems to be the popular choice. I think it's the most open-source friendly out of the bunch. I will keep function calling in mind as I design some of our models! Thanks for bringing that up.

[–] Blaed 2 points 1 year ago (1 children)

I appreciate your comment! It seems like we're going the fine-tuning route. I think it's the best way to do it too. I'm still glad I floated around the foundation model idea. We'll get one of our own eventually!

Welcome to the show! Enthusiast or not, you are part of [email protected]. Your input is valued and your curiosity is encouraged!

[–] Blaed 3 points 1 year ago

It seems like we'll be starting with Mistral - which means the model will be completely open-source under the Apache 2.0 License.

All fine-tunings I release under fosai would be licensed under the same Apache 2.0 agreement, giving you and everyone else complete permissions to modify, download, distribute, and deploy this model as you see fit. It would make the model commercially viable out-of-the-box without any restrictions set by a corporation or entity.

I'm also not a copyright lawyer, so someone correct me If I'm wrong here but if I fine-tune Mistral (which I probably will) and also release the derivative under the Apache 2.0 license - you own the version you choose to download completely. You don't need to adhere to a usage policy. You are still responsible for what you end up doing with your model (within all local applicable laws), but you also don't have to worry about Meta (or some other entity) revoking or changing their policy/usage/terms at some point in the future. You are free to do whatever you want with an Apache licensed model.

At the end of the day, Llama 2 is owned and distributed by Meta AI, which has some of those restrictions I mentioned, even though it is somewhat open-source. Here is the license. Some notes from it that might be worth mentioning:

  • You need to credit Meta whenever you share Llama 2 by including a specific notice.
  • You have to follow all laws and regulations when using Llama 2 and also adhere to Meta's usage policy.
  • You can't use Llama 2 to make or improve other similar software (large language models), except Llama 2 itself or things derived from it.
  • If your company or its affiliates have more than 700 million users a month, you can't just use this agreement. You have to ask Meta for special permission.
[–] Blaed 2 points 1 year ago

I wouldn’t want risk a legal battle with a company the size of Meta, so I’d vote for the other options just to be one the safe side.

Completely reasonable, I agree.

Do you have the resources for this to be a viable option?

Where there's a will, there's a way. I could muster the resources for a foundation model, but it's definitely not the most optimal option we have at our disposal. The original plan was a.) fine-tune a small series (short-term) b.) release a foundation model (long-term). I only recently considered skipping Plan A, but I'm glad I've got feedback to prevent me from doing otherwise. Would've enjoyed the process nonetheless.

Are you confident that the end result will be better than Mistral? If not, why spend that much on creating something equivalent or possibly even inferior?

Of course not. I don't do this to be the best. I offer to do this to understand. To document how to build and release a foundation model from start to finish is knowledge that could be valuable to someone else - which is why I was willing to skip ahead if that was a topic others wanted to dive more into. For me, it's more about the friends we make along the way. There is grace in polishing a product and being the best, but I'd like to think there is also something special in doing something just to document it for others. There is something fulfilling exploring a new frontier with nothing but sheer curiosity.

Then there’s also the question of how long a model is going to be relevant before some other new model with all the latest innovations is released and makes everything else look outdated… Even if you can create a model which rivals llama-2 and mistral now, are you going to create a new one to compete with llama-3 and mistral-2 when those come along?

I also don't do this to be relevant. To be a part of the this is enough for me. In my studies, I have found something bigger than me - I see myself doing this for many years so I know I'll be around to see it evolve and current technologies become irrelevant in time. If you consider existing alongside these models as 'competing' then yes, I would be doing that I suppose.

Sorry for the negativity but I think creating a base model sounds likely to be a massive waste of resources. If you have a lot of time and money to throw at this project, I think it would be much better spent on fine-tuning existing models.

Don't worry, it was very great feedback. Exactly why I made this post! I'm glad you made all your points. It's the same logic I had (and the same logic I was willing to throw aside for others). At this point, it seems like fine-tuning is what most of you want to see. So fine-tuning it shall be!

[–] Blaed 6 points 1 year ago* (last edited 1 year ago) (5 children)

This will be a fine-tuned model, so it may inherit some of the permissions and license agreements as its foundation model and have other implications depending on your country or local law.

You are correct, if we chose Llama 2 - the fine-tune derivative may be subject to their original license terms. However, Apache 2.0 would apply and transfer to something like a fine-tuned version of Mistral, since its base license is also Apache 2.0.

If there is enough support - I'd be more than open to creating an entirely new foundation model family. This would be a larger undertaking than this initial fine-tuning deployment, but building a completely free FOSAI foundation family of models was the penultimate goal of this project so if this garners enough attention I could absolutely put energy and focus into creating another Mistral-like product instead of splashing around with fine-tuning.

Whatever would help everyone the most! I like where you're thinking though, I'm going to update the thread to include an option to vote for a new foundation family instead. At the end of the day, it's likely I'll do all of the above - I'm just not sure in what order yet..

[–] Blaed 3 points 1 year ago* (last edited 1 year ago) (1 children)

I have come to believe Moore's law is finite, and we're starting to see the exponential end of it. This leads me to believe (or want to believe) there are other looming breakthroughs for compute, optimization, and/or hardware on the horizon. That, or crazy powerful GPUs are about to be a common household investment.

I keep thinking about what George Hotz is doing in regards to this. He explained on his podcast with Lex Fridman that there is much to be explored in optimization, both with quantization of software and acceleration of hardware.

His idea of 'commoditize the petabyte' is really cool. I think it's worth bringing up here, especially given the fact it appears one of his biggest goals right now is solving the at-home compute problem. But in a way that you could actually run something like a 180B model in-house no problem.

George Hotz' tinybox

($15,000)

  • 738 FP16 TFLOPS
  • 144 GB GPU RAM
  • 5.76 TB/s RAM bandwidth
  • 30 GB/s model load bandwidth (big llama loads in around 4 seconds)
  • AMD EPYC CPU
  • 1600W (one 120V outlet)
  • Runs 65B FP16 LLaMA out of the box (using tinygrad, subject to software development risks)

You can pre-order one now. You have $15k laying around, right? Lol.

It's definitely not easy (or cheap) now, but I think it's going to get significantly easier to build and deploy large models for all kinds of personal use cases in our near and distant futures.

If you're serving/hosting models, it's also worth checking out vLLM if you haven't already: https://github.com/vllm-project/vllm

[–] Blaed 2 points 1 year ago

Loved to read everyone's comments on this one. If you're here and reading this post now, check out this related thread - you might be interested!

57
submitted 1 year ago* (last edited 1 year ago) by Blaed to c/fosai
 

Hey everyone!

I think it's time we had a fosai model on HuggingFace. I'd like to start collecting ideas, strategies, and approaches for fine-tuning our first community model.

I'm open to hearing what you think we should do. We will release more in time. This is just the beginning.

For now, I say let's pick a current open-source foundation model and fine-tune on datasets we all curate together, built around a loose concept of using a fine-tuned LLM to teach ourselves more bleeding-edge technologies (and how to build them using technical tools and concepts).

FOSAI is a non-profit movement. You own everything fosai as much as I do. It is synonymous with the concept of FOSS. It is for everyone to champion as they see fit. Anyone is welcome to join me in training or tuning using the workflows I share along the way.

You are encouraged to leverage fosai tools to create and express ideas of your own. All fosai models will be licensed under Apache 2.0. I am open to hearing thoughts if other licenses should be considered.


We're Building FOSAI Models! πŸ€–

Our goal is to fine-tune a foundation model and open-source it. We're going to start with one foundation family with smaller parameters (7B/13B) then work our way up to 40B (or other sizes), moving to the next as we vote on what foundation we should fine-tune as a community.


Fine-Tuned Use Case β˜‘οΈ

Technical

  • FOSAI Model Idea #1 - Research & Development Assistant
  • FOSAI Model Idea #2 - Technical Project Manager
  • FOSAI Model Idea #3 - Personal Software Developer
  • FOSAI Model Idea #4 - Life Coach / Teacher / Mentor
  • FOSAI Model Idea #5 - FOSAI OS / System Assistant

Non-Technical

  • FOSAI Model Idea #6 - Dungeon Master / Lore Master
  • FOSAI Model Idea #7 - Sentient Robot Character
  • FOSAI Model Idea #8 - Friendly Companion Character
  • FOSAI Model Idea #9 - General RPG or Sci-Fi Character
  • FOSAI Model Idea #10 - Philosophical Character

OR

FOSAI Foundation Model β˜‘οΈ


Foundation Model β˜‘οΈ

(Pick one)

  • Mistral
  • Llama 2
  • Falcon
  • ..(Your Submission Here)

Model Name & Convention

  • snake_case_example
  • CamelCaseExample
  • kebab-case-example

0.) FOSAI β˜‘οΈ

  • fosai-7B
  • fosai-13B

1.) FOSAI Assistant β˜‘οΈ

  • fosai-assitant-7B
  • fosai-assistant-13B

2.) FOSAI Atlas β˜‘οΈ

  • fosai-atlas-7B
  • fosai-atlas-13B

3.) FOSAI Navigator β˜‘οΈ

  • fosai-navigator-7B
  • fosai-navigator-13B

4.) ?


Datasets β˜‘οΈ

  • TBD!
  • What datasets do you think we should fine-tune on?

Alignment β˜‘οΈ

To embody open-source mentalities, I think it's worth releasing both censored and uncensored versions of our models. This is something I will consider as we train and fine-tune over time. Like any tool, you are responsible for your usage and how you choose to incorporate into your business and/or personal life.


License β˜‘οΈ

All fosai models will be licensed under Apache 2.0. I am open to hearing thoughts if other licenses should be considered.

This will be a fine-tuned model, so it may inherit some of the permissions and license agreements as its foundation model and have other implications depending on your country or local law.

Generally speaking, you can expect that all fosai models will be commercially viable through the selection process of its foundation family and the post-processing steps that are fine-tuning the model.


Costs

I will be personally covering all training and deployment costs. This may change if I choose to put together some sort of patronage, but for now - don't worry about this. I will be using something like RunPod or some other custom deployed solution for training.


Cast Your Votes! β˜‘οΈ

Share Your Ideas & Vote in the Comments Below! βœ…

What do you want to see out of this first community model? What are some of the fine-tuning ideas you've wanted to try, but never had the time or chance to test? Let me know in the comments and we'll brainstorm together.

I am in no rush to get this out, so I will leave this up for everyone to see and interact with until I feel we have a solid direction we can all agree upon. There will be plenty of more opportunities to create, curate, and customize more fosai models I plan to release in the future.

Update [10/25/23]: I may have found a fine-tuning workflow for both Llama (2) and Mistral, but I haven't had any time to validate the first test run. Once I have a chance to do this and test some inference I'll be updating this post with the workflow, the models, and some sample output with example datasets. Unfortunately, I have ran out of personal funds to allocate to training, so it is unsure when I will have a chance to make another attempt at this if this first attempt doesn't pan out. Will keep everyone posted as we approach the end of 2023.

[–] Blaed 1 points 1 year ago

I totally forgot to include vLLM!

If you're building, deploying, or hosting LLMs, you should definitely check this out.

https://github.com/vllm-project/vllm

 

cross-posted from: https://lemmy.world/post/5965315

πŸ€– Happy FOSAI Friday! πŸš€

Friday, September 29, 2023

HyperTech News Report #0002

Hello Everyone!

Welcome back to the HyperTech News Report! This week we're seeing some really exciting developments in futuristic technologies. With more tools and methods releasing by the day, I feel we're in for a renaissance in software. I hope hardware is soon to follow.. but I am here for it! So are you. Brace yourselves. Change is coming! This next year will be very interesting to watch unfold.

Table of Contents

Community Changelog

  • Cleaned up some old content (let me know if you notice something that should be archived or updated)

Image of the Week

This image of the week comes from a DALL-E 3 demonstration by Will Depue. This depicts a popular image for diffusion models benchmarks - the astronaut riding a horse in space. Apparently this was hard to get right, and others have had trouble replicating it - but it seems to have been generated by DALL-E 3 nevertheless. Curious to see how it stacks up against other diffusers when its more widely available.

New Foundation Model!

There have been many new models hitting HuggingFace on the daily. The recent influx has made it hard to benchmark and keep up with these models - so I will be highlighting a hand select curated week-by-week, exploring these with more focus (a few at a time).

If you have any model favorites (or showcase suggestions) let me know what they are in the comments below and I'll add them to the growing catalog!

This week we're taking a look at Mistral - a new foundation model with a sliding attention mechanism that gives it advantages over other models. Better yet - the mistral.ai team released this new model under the Apache 2.0 license. Massive shoutout to this team, this is huge for anyone who wants more options (commercially) outside of Llama 2 and Falcon families.

From Mistralai:

The best 7B, Apache 2.0.. Mistral-7B-v0.1 is a small, yet powerful model adaptable to many use-cases. Mistral 7B is better than Llama 2 13B on all benchmarks, has natural coding abilities, and 8k sequence length. It’s released under Apache 2.0 licence, and we made it easy to deploy on any cloud.

Learn More

Mistralai

TheBloke (Quantized)

More About GPTQ

More About GGUF

Metaverse Developments

Mark Zuckerberg had his third round interview on the Lex Fridman podcast - but this time, in the updated Metaverse. This is pretty wild. We seem to have officially left uncanny valley territory. There are still clearly bugs and improvements to be made - but imagine the possibilities of this mixed reality technology (paired with VR LLM applications).

The type of experiences we can begin to explore in these digital realms are going to evolve into things of true sci-fi in our near future. This is all very exciting stuff to look forward to as AI proliferates markets and drives innovation.

What do you think? Zuck looks more human in the metaverse than in real life.. mission.. success?

Click here for the podcast episode.

NVIDIA NeMo Guardrails

If you haven't heard about NeMo Guardrails, you should check it out. It is a new library and approach for aligning models and completing functions for LLMs. It is similar to LangChain and LlamaIndex, but uses an in-house developed language from NVIDIA called 'colang' for configuration, with NeMo Guardrail libraries in python friendly syntax.

This is still a new and unexplored tool, but could provide some interesting results with some creative applications. It is also particularly powerful if you need to align enterprise LLMs for clients or stakeholders.

Learn More

Tutorial Highlights

Mistral 7B - Small But Mighty πŸš€ πŸš€

Chatbots with RAG: LangChain Full Walkthrough

NVIDIA NeMo Guardrails: Full Walkthrough for Chatbots / AI

Author's Note

This post was authored by the moderator of [email protected] - Blaed. I make games, produce music, write about tech, and develop free open-source artificial intelligence (FOSAI) for fun. I do most of this through a company called HyperionTechnologies a.k.a. HyperTech or HYPERION - a sci-fi company.

Thanks for Reading!

If you found anything about this post interesting, consider subscribing to [email protected] where I do my best to keep you informed about free open-source artificial intelligence as it emerges in real-time.

Our community is quickly becoming a living time capsule thanks to the rapid innovation of this field. If you've gotten this far, I cordially invite you to join us and dance along the path to AGI and the great unknown.

Come on in, the water is fine, the gates are wide open! You're still early to the party, so there is still plenty of wonder and discussion yet to be had in our little corner of the digiverse.

This post was written by a human. For other humans. About machines. Who work for humans for other machines. At least for now...

Until next time!

Blaed

 

cross-posted from: https://lemmy.world/post/5965315

πŸ€– Happy FOSAI Friday! πŸš€

Friday, September 29, 2023

HyperTech News Report #0002

Hello Everyone!

Welcome back to the HyperTech News Report! This week we're seeing some really exciting developments in futuristic technologies. With more tools and methods releasing by the day, I feel we're in for a renaissance in software. I hope hardware is soon to follow.. but I am here for it! So are you. Brace yourselves. Change is coming! This next year will be very interesting to watch unfold.

Table of Contents

Community Changelog

  • Cleaned up some old content (let me know if you notice something that should be archived or updated)

Image of the Week

This image of the week comes from a DALL-E 3 demonstration by Will Depue. This depicts a popular image for diffusion models benchmarks - the astronaut riding a horse in space. Apparently this was hard to get right, and others have had trouble replicating it - but it seems to have been generated by DALL-E 3 nevertheless. Curious to see how it stacks up against other diffusers when its more widely available.

New Foundation Model!

There have been many new models hitting HuggingFace on the daily. The recent influx has made it hard to benchmark and keep up with these models - so I will be highlighting a hand select curated week-by-week, exploring these with more focus (a few at a time).

If you have any model favorites (or showcase suggestions) let me know what they are in the comments below and I'll add them to the growing catalog!

This week we're taking a look at Mistral - a new foundation model with a sliding attention mechanism that gives it advantages over other models. Better yet - the mistral.ai team released this new model under the Apache 2.0 license. Massive shoutout to this team, this is huge for anyone who wants more options (commercially) outside of Llama 2 and Falcon families.

From Mistralai:

The best 7B, Apache 2.0.. Mistral-7B-v0.1 is a small, yet powerful model adaptable to many use-cases. Mistral 7B is better than Llama 2 13B on all benchmarks, has natural coding abilities, and 8k sequence length. It’s released under Apache 2.0 licence, and we made it easy to deploy on any cloud.

Learn More

Mistralai

TheBloke (Quantized)

More About GPTQ

More About GGUF

Metaverse Developments

Mark Zuckerberg had his third round interview on the Lex Fridman podcast - but this time, in the updated Metaverse. This is pretty wild. We seem to have officially left uncanny valley territory. There are still clearly bugs and improvements to be made - but imagine the possibilities of this mixed reality technology (paired with VR LLM applications).

The type of experiences we can begin to explore in these digital realms are going to evolve into things of true sci-fi in our near future. This is all very exciting stuff to look forward to as AI proliferates markets and drives innovation.

What do you think? Zuck looks more human in the metaverse than in real life.. mission.. success?

Click here for the podcast episode.

NVIDIA NeMo Guardrails

If you haven't heard about NeMo Guardrails, you should check it out. It is a new library and approach for aligning models and completing functions for LLMs. It is similar to LangChain and LlamaIndex, but uses an in-house developed language from NVIDIA called 'colang' for configuration, with NeMo Guardrail libraries in python friendly syntax.

This is still a new and unexplored tool, but could provide some interesting results with some creative applications. It is also particularly powerful if you need to align enterprise LLMs for clients or stakeholders.

Learn More

Tutorial Highlights

Mistral 7B - Small But Mighty πŸš€ πŸš€

Chatbots with RAG: LangChain Full Walkthrough

NVIDIA NeMo Guardrails: Full Walkthrough for Chatbots / AI

Author's Note

This post was authored by the moderator of [email protected] - Blaed. I make games, produce music, write about tech, and develop free open-source artificial intelligence (FOSAI) for fun. I do most of this through a company called HyperionTechnologies a.k.a. HyperTech or HYPERION - a sci-fi company.

Thanks for Reading!

If you found anything about this post interesting, consider subscribing to [email protected] where I do my best to keep you informed about free open-source artificial intelligence as it emerges in real-time.

Our community is quickly becoming a living time capsule thanks to the rapid innovation of this field. If you've gotten this far, I cordially invite you to join us and dance along the path to AGI and the great unknown.

Come on in, the water is fine, the gates are wide open! You're still early to the party, so there is still plenty of wonder and discussion yet to be had in our little corner of the digiverse.

This post was written by a human. For other humans. About machines. Who work for humans for other machines. At least for now...

Until next time!

Blaed

[–] Blaed 1 points 1 year ago
[–] Blaed 1 points 1 year ago
[–] Blaed 1 points 1 year ago

This was really nice to celebrate with the (17?) people who saw this post and shared this moment with me. I have no idea what this future will hold, but I'm glad I started this community. I'm going to delete (or archive?) this post now. It feels too selfish to keep up. If you caught this comment, you found an easter egg! Congrats, digital sleuth.

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