Podcasts about sgn

On Scottish Grand National weekend. Nick was joined in the studio by Alan Johns, Saffie Osborne & Ralph Beckett. We reflected on the SGN, chatting with winning jockey Harry Cobden. We also caught up with Juddmonte's European Racing Manager Barry Mahon, to reflect upon Jonquil's impressive win the Greenham Stakes. As well as plenty of other lively racing discussions.

Dad & Broc are joined by Dave Heilman from Dynasty Trade Calculator & SGN to talk about. 17 #dynastytrades involving the elite 2024 Rookie class

Asbjørn er skiguide, frikjører og Norrøna-ambassadør. Vi tar praten om blant annet skred, Norrøna, skikjøring, guiding, ulykker og toucher innom skisportens fremtid.Norrøna, frikjøring, SGN, Dynafit, topptur, randonee Hosted on Acast. See acast.com/privacy for more information.

In this week's Talking Tech feature, Josh is joined by SGN's Social Impact Programme Lead, Dan Edwards. In the interview they discuss the gas distribution company's (SGN) efforts to raise public awareness about the dangers of carbon monoxide (CO) poisoning, often known as the "silent killer" due to its invisible, odourless, and tasteless presence.

Avante, HQueiros! Hoje vamos bater um papo com Leandro Cruz que tem entregado um catálogo excepcional e tratamento diferenciado através da Rede SGN, focada em material pouco encontrado nas comic books tradicionais. "Rede SGN de Quadrinhos: Para Leitores além do Óbvio!" Link para a loja: https://redesgn.lojavirtualnuvem.com.br/ Link para o Instagram: https://www.instagram.com/redesgnhqs/ Link para demais plataformas aonde a SGN se encontra: https://linktr.ee/redesgn Participe do nosso grupo de Whatsapp onde diariamente interagimos discutindo esses HQs que tanto amamos: https://chat.whatsapp.com/HE46Hdfp0Ua6wkXM1nTwHb Participe do nosso podcast, mande sua mensagem para contato@hqueiros.com.br ou envie um áudio interagindo através do nosso Instagram. SEJA NOSSO PADRINHO NO CATARSE: https://www.catarse.me/hqueiros

There are many things you have to get right as a business owner, and the number one thing is running a healthy business. Tom shares the top warning signs that your business may be sick and in need of some TLC. He wants you to pay attention to these signs, examine them in your business, and implement the necessary changes to keep your business fighting fit. Episode Highlights:[00:00] - Intro[01:13] - Run a healthy business[01:40] - 8 signs of a sick and dying business[02:42] - #Sign1: Lack of cash flow[03:56] - #Sign2: Not paying yourself fairly[06:10] - #Sgn 3: Low pre-tax profit[07:20] - #Sign 4: Borrowing money to survive[09:14] - #Sign 5: Low-quality or inconsistent leads[10:56] - #Sign 6: Busy but broke[12:40] - #Sign 7: Outdated books[14:34] - #Sign 8: Irregular meetings[15:48] - Closing remarksIf you need help with your business, visit https://thecontractorfight.com/help/ to schedule a call and see if you qualify for the Battleground program. Resources:

The pace of advances in AI is faster than anything we've ever seen in human history. Nathan explains how this can create a lot of overwhelm for leaders, leading to a disparate kind of view, with leaders saying, “I don't know what the future looks like if I use AI but I know what it looks like if I don't.” The sentiment is that we know we have to do something with AI, we just don't know what.If it sounds familiar to you, tune into the episode. Chris Meyer zu Natrup, Managing Director at MzN, and Nathan Chappell, Senior Vice President at DonorSearch AI, discuss:What AI can do for a nonprofit organization, not tomorrow, but today.The difference between predictive AI and generative AI, and how NGOs can use both together to serve their mission in a much more strategic and precise way.The steps every leader needs to take with AI. “I would identify where my biggest gap is, I wouldn't look at AI as this umbrella thing that could solve all my problems, I would get very specific on one gap or challenge that I have in my organization and then and only then would I look at the types of AI technologies.” Why Nathan believes that AI is the only scalable solution to reverse declines in charitable giving. There is a systemic decline in the number of people who are giving to charity in almost most developed nations, but Nathan describe two ways that AI can work to reverse this:It can create so much efficiency that an organization can now offload mundane tasks and instead spend more time human to human.By using AI at scale, nonprofits organizations will more intimately know their audience by using thousands of data points and will be able to identify those donors that will have the greatest lifetime value. Could AI actually make us more human and more empathetic by cutting through the dead digital overload that we are exposed to every day? What responsible AI looks like for humanity, and the outsized responsibility that the nonprofit sector has for ensuring we prioritize safety over scalability. Nathan closed by quoting Charlie Munger, Warren Buffett's business partner, who said “show me the incentives and I'll show you the outcome”. If we look at the AI arms race, scale has been prioritized over safety almost every time. What do we think the incentive is for the nonprofit sector? ResourcesThe Generosity Crisis The Case for Radical Connection to Solve Humanity's Greatest Challenges, by Nathan Chappell, Brian Crimmins and Michael Ashley.Sgn up for MzN's free webinar on proposal writing and AI - link to the webinarMore on Nathan:Nathan Chappell is a Senior Vice President at DonorSearch AI, where he leads AI deployments for many of the largest nonprofit organizations in the United States. With over 10 years of experience in nonprofit leadership and fundraising, he is a co-author of the award-winning book, The Generosity Crisis: The Case for Radical Connection to Solve Humanity's Greatest Challenges. He is also an inventor of multiple AI patents and a certified fundraising executive.Nathan is passionate about the responsible use of AI for social good and the ethical implications of AI for philanthropy. He is the co-founder of Fundraising.AI, a collaborative initiative that aims to guide best practices and accelerate the impact of AI on advancement. He is also a frequent public speaker, a Forbes Technology Council member, and a recipient of the 40 Over Forty Award for his contributions to the nonprofit sector. Nathan's mission is to reignite generosity and transform the future of fundraising through AI.

This week on The Exit: Suleman “Suli” Ali was introduced to the world of business M&A when he was just eight years old helping his father find and buy their family's first business. However, it wasn't until years later into his early 20s and his first job at Microsoft that he determined that corporate life was not for him and he answered his calling to a life of entrepreneurship. During this time, the Facebook app marketplace was in its early stages and he went all in on becoming a leader in the Facebook app space. He and his team would go on to build and exit two mobile gaming businesses, Esgut (acquired by SGN) and then TinyCo (acquired by Jam City). Two exits and decades of experience later in building both bootstrapped and VC-backed businesses, he emphasizes the importance of building businesses that will continue to perform through increased competition and marketing costs. Through it all, he hopes business owners will do what he wishes he did for himself: to enjoy the journey as you're building and growing the business, and not just at the exit. For more details and insights from his entrepreneurial journey, listen to the latest episode of The Exit. Suli Ali is a serial investor and entrepreneur who has started and sold 2 startups with Esgut being acquired by SGN, and TinyCo being acquired by Jam City. Suli has been an angel investor in over 100 startups and LP in 25+ venture capital funds. Through Ali Capital, along with his brother Moiz, Suli has a passion for investing with founders who see a future that few others can see. LinkedIn: linkedin.com/in/sulemanali Website: https://www.ali-capital.co/ X (Twitter) - https://twitter.com/sulemanali For a Free Flippa Business Valuation: https://flippa.com/exit -- The Exit—Presented By Flippa: A 30-minute podcast featuring expert entrepreneurs who have been there and done it. The Exit talks to operators who have bought and sold a business. You'll learn how they did it, why they did it, and get exposure to the world of exits, a world occupied by a small few, but accessible to many. To listen to the podcast or get daily listing updates, click on flippa.com/the-exit-podcast/

Paul is joined by Paolo, one his best friends, to compare notes on a big tour of Cambodia and Vietnam (Paul flew to Cambodia, Paolo flew to Vietnam and Cambodia) with many airports, and airlines along the way (not sure about the water on-board a Cambodia Angkor Air). Paul had his worst fly ever (it was no fault of Etihad, just his own!!) and circles the 380 lounge table forever like an animal, whilst Paolo has the most epic security check experience at Sihanouk International Airport (he's a giant, after all). Cambodia is building huge airports, the future Phnom Penh (still in construction), and the just-opened massive Siem Reap International which we both were pretty staggered with. Are turbulences getting worse, and should the pilot give more explanation when we hit them (Paolo compares them as being rock cradled to sleep in business class!). Does landing at SGN remind you a little of Kai Tak ("dodging the washing lines across the apartments"). Paul and Paolo disagree on BKK, but, yeah, a bit of window cleaning would be good (but the Thai government agrees with Paul, it needs a refurb!). Do you really want to board a Wamos Air? 

Welcome back everyone for this special episode of CCC! I am joined by Alex Stiff and Cristy Phillips from SGN and we discuss the conspiracy behind the sixties assassinations.... from JFK to Malcolm X, to MLK and Robert Kennedy. Find out what we think happened..... was it aliens..... The CIA.... Communists........ or did JFK's head just explode!!!!The CCC merch store is now LIVE! Get you a brand new t-shirt, drinking glass, sticker pack, and way more!! https://somethinggoodnetwork.bigcartel.com/category/the-cryptid-consparacy-cultListen to other shows on the SOMETHING GOOD NETWORK: https://linktr.ee/somethinggoodnetwork Join our Discord: https://discord.gg/Ge53Nu9

Your eyes do not deceive you! This is a very special round table episode covering all of the shows here on the network! Featuring Alex Stiff, Chris Morrison, Dante Carter, Mikey Black, and David Hammer; we cover the gambit when it comes to ridiculous topics and conversations! We try some tasty alcohol, discuss joke stealing, the similarities between Adam Sandler and KISS, Chris tries to quiz us on some history questions, and we also discuss the new Deadpool and Fantastic Four news! Is that the return of ASMR I hear? Oh no...The SGN merch store is now LIVE with shirts, drinking glasses, hoodies and more! - https://somethinggoodnetwork.bigcartel.comListen to other shows on the SOMETHING GOOD NETWORK: https://linktr.ee/somethinggoodnetwork Join our Discord: https://discord.gg/Ge53Nu9

Your eyes do not deceive you! This is a very special round table episode covering all of the shows here on the network! Featuring Alex Stiff, Chris Morrison, Dante Carter, Mikey Black, and David Hammer; we cover the gambit when it comes to ridiculous topics and conversations! We try some tasty alcohol, discuss joke stealing, the similarities between Adam Sandler and KISS, Chris tries to quiz us on some history questions, and we also discuss the new Deadpool and Fantastic Four news! Is that the return of ASMR I hear? Oh no...The SGN merch store is now LIVE with shirts, drinking glasses, hoodies and more! - https://somethinggoodnetwork.bigcartel.comListen to other shows on the SOMETHING GOOD NETWORK: https://linktr.ee/somethinggoodnetwork Join our Discord: https://discord.gg/Ge53Nu9

On a very special episode of CCC, we sit down with FIREBALL!!!!!! A friend of the something good network and talk about all things conspiracy.... this episode goes off the rails, but come on this wild ride of an episode and don't forget about all the other great shows from SGN!!!The CCC merch store is now LIVE! Get you a brand new t-shirt, drinking glass, sticker pack, and way more!! https://somethinggoodnetwork.bigcartel.com/category/the-cryptid-consparacy-cultListen to other shows on the SOMETHING GOOD NETWORK: https://linktr.ee/somethinggoodnetwork Join our Discord: https://discord.gg/Ge53Nu9

The Latent Space crew will be at NeurIPS on Tuesday! Reach out with any parties and papers of interest. We have also been incubating a smol daily AI Newsletter and Latent Space University is making progress.Good open models like Llama 2 and Mistral 7B (which has just released an 8x7B MoE model) have enabled their own sub-industry of finetuned variants for a myriad of reasons:* Ownership & Control - you take responsibility for serving the models* Privacy - not having to send data to a third party vendor* Customization - Improving some attribute (censorship, multiturn chat and chain of thought, roleplaying) or benchmark performance (without cheating)Related to improving benchmark performance is the ability to use smaller (7B, 13B) models, by matching the performance of larger models, which have both cost and inference latency benefits.Core to all this work is finetuning, and the emergent finetuning library of choice has been Wing Lian's Axolotl.AxolotlAxolotl is an LLM fine-tuner supporting SotA techniques and optimizations for a variety of common model architectures:It is used by many of the leading open source models:* Teknium: OpenHermes, Trismigestus, CollectiveCognition* OpenOrca: Mistral-OpenOrca, Mistral-SlimOrca* Nous Research: Puffin, Capybara, NousHermes* Pygmalion: Mythalion, Pygmalion* Eric Hartford: Dolphin, Samantha* DiscoResearch: DiscoLM 120B & 70B* OpenAccess AI Collective: Manticore, Minotaur, Jackalope, HippogriffAs finetuning is very formatting dependent, it also provides prompt interfaces and formatters between a range of popular model formats from Stanford's Alpaca and Steven Tey's ShareGPT (which led to Vicuna) to the more NSFW Pygmalion community.Nous Research MeetupWe last talked about Nous at the DevDay Recap at the e/acc “banger rave”. We met Wing at the Nous Research meetup at the a16z offices in San Francisco, where they officially announced their company and future plans:Including Nous Forge:Show NotesWe've already covered the nuances of Dataset Contamination and the problems with “Open Source” in AI, so we won't rehash those topics here but do read/listen to those if you missed it.* Axolotl GitHub and Discord* The Flan paper and dataset* StackLlama model and blogpost* Multipack paper* Our episode with Tri Dao* Mamba state space models - Tri Dao and Albert GuTimestamps* [00:00:00] Introducing Wing* [00:02:34] SF Open Source AI Meetup* [00:04:09] What is Axolotl?* [00:08:01] What is finetuning?* [00:08:52] Open Source Model Zoo* [00:10:53] Benchmarks and Contamination* [00:14:29] The Case for Open Source AI* [00:17:34] Orca and OpenOrca* [00:23:36] DiscoLM and Model Stacking* [00:25:07] Datasets and Evals over Models* [00:29:15] Distilling from GPT4* [00:33:31] Finetuning - LoRA, QLoRA, ReLoRA, GPTQ* [00:41:55] Axolotl vs HF Transformers* [00:48:00] 20x efficiency with StackLlama and Multipack* [00:54:47] Tri Dao and Mamba* [00:59:08] Roadmap for Axolotl* [01:01:20] The Open Source AI CommunityTranscript[00:00:00] Introducing Wing Lian[00:00:00] ​[00:00:00] swyx: Welcome to Latent Space, a special edition with Wing Lien, but also with our new guest host, Alex. Hello, hello. Welcome, welcome. Again, needs no introduction. I think it's like your sixth time on Latent Space already. I think so, yeah. And welcome, Wing. We just met, but you've been very prolific online. Thanks for having me.[00:00:30] Yeah. So you are in town. You're not local. You're in town. You're from Minneapolis?[00:00:35] Wing Lian: Annapolis. Annapolis. It's funny because a lot of people think it's Indianapolis. It's I've got Minneapolis, but I used to live out at least in the San Francisco Bay Area years ago from like 2008 to 2014. So it's fairly familiar here.[00:00:50] swyx: Yep. You're the maintainer of Axolotl now, which we'll get into. You're very, very prolific in the open source AI community, and you're also the founder of the Open Access AI Collective. Yeah. Cool. Awesome. Maybe we can go over a little bit of your backgrounds into tech and then coming into AI, and then we'll cover what[00:01:06] Wing Lian: happens and why you're here.[00:01:08] Yeah. So. Back on tech, so I started years ago, I started way back when I was scraping, Apartment websites for listings and then, and then building like SEO optimized pages and then just throwing Google AdSense on it.[00:01:24] And that got me through like college basically. Is[00:01:27] swyx: that decent money? And what year[00:01:28] Wing Lian: was this? Like 2004, 2005. Yeah, that's decent money. It's like thousand bucks a month. But as a college student, that's like. Gravy. Really good money, right? So, and then there's just too much competition It's just sort of like died off. I was writing stuff in like Perl back then using like like who nobody hosted anything on Perl anymore, right? Still did a little bit more like computer tech support and then software, and web more professionally.[00:01:54] So I spent some time working on applications in the blood industry. I came out to San Francisco for, I was at SGN, so Social Gaming Network, as a startup. They started doing, with Facebook apps, and then they pivoted into doing mobile apps. And then, from there, I spent time.[00:02:14] I've quite a few more startups since then and in the last few years I've been in the music space So like I was at United Masters for a while and then past year I've been at SoundCloud, but not doing that anymore and now that I have a lot more time It's just like all right.[00:02:30] We're going full bore on axolotl and we're gonna we're gonna crush AI So yeah,[00:02:34] SF Open Source AI Meetup[00:02:34] swyx: totally you so you're here in town for the open source. Yeah, I meet up that we had yesterday Yep, yeah, that was amazing. Yeah, it was a big collection. Olama, Noose Research, Alignment Lab, Anyone else that I missed? I mean, Jeremy Howard is his own thing.[00:02:47] Yeah.[00:02:49] And Alex, you're also there. You love to bring SF to the world. Your takes?[00:02:55] Alex Volkov: It's incredible that we recorded a Thursday Eye episode after that one. And LDJ, who's usually co hosts Thursday Eye, just like briefly mentioned, Oh yeah, I talked about it.[00:03:04] Like, I saw Karpathy, and then I talked to Jeremy Howard, and the guy from Mistral came in, and it's like, He's talking about all these, titans of industry, basically, that outside of SF, You just don't meet casually hanging out in the same space. You can't, pull somebody. He ran into the Laylow from Mistral, he ran into him while, drinking water.[00:03:20] He didn't even know he was there. It's just, that type of stuff is really hard to find outside of SF. So, absolutely, absolutely great. And also, presentations from Alignment Labs, presentations from News Research, news issues, talked about. Forge, and some of[00:03:33] swyx: the other stuff they announced. We can say now they're officially a company.[00:03:36] I met Technium.[00:03:37] He[00:03:37] Alex Volkov: came over here. He didn't want to get recorded. But maybe.[00:03:41] Wing Lian: We'll wear him down at some point. Yeah, I'm excited for Forge. They've positioned it as this agentic sort of framework where it's just Drag and drop things and, fill in text with where you want to inject different variables and it opens up all of these potentials for data pipelines now, right?[00:03:56] And using your own local LLMs and not relying on GPT 4 or anything like that. Yeah, yeah,[00:04:02] swyx: good stuff. Okay, so let's maybe go into the Axolotl origin story and then we have, we have some intro or background.[00:04:09] What is Axolotl?[00:04:09] swyx: To do on like the open source model universe and also on fine tuning, but maybe just, since you're talking about your personal journey, what was your personal journey into[00:04:18] Wing Lian: axolotl?[00:04:19] Yeah, so my personal journey started like back in mid March, completely unrelated to AI and axolotl. And it really started, I fell while skiing, I torqued. Great 3 MCL sprain and being sort of like an active person that can no longer be active because the two, couldn't play soccer, because that is requires to have having knees until I, it's healed.[00:04:42] So I. I decided I needed to find something to do to take up my free time. And that became, well, let's learn how to train in, these language models. It was everywhere. So I was like, all right, I'm just going to sit down, learn. I think I used like other, I think I was using like Alpacalora.[00:05:00] Cause I think the Alpaca paper had just came out, come out then. So I was like using Alpacalora repo and sort of like learning how to use like. None of us were like GPU rich back then, and none of us, most of us still we're still all GPU poor, but I was doing what was it, like 4 bit, Alpaca Lord, there was like a 4 bit version where we were doing quant, or 8, no, 8 bit quantizations, and then I think they had released QLOR a little bit later, and I think right when, before QLOR came out, I was already starting to do fine tunes, but having this need to sort of like mix data sets together, and If you've ever looked at all the various different datasets available on HuggingFace, they all have various different prompt formats, and, it's sort of a nightmare, and then I think the other piece is if you've ever tried to fine tune, at least Back then probably the ecosystem's a little better now.[00:05:54] Everybody required that you say, alright, you put your hyperparameters as command line arguments. And so it's always like, well, I now have to go copy and paste my previous thing and to change things out. And I really wanted it. to be in a YAML file because it was more portable and reproducible.[00:06:09] So I was doing that and then the QLOR paper came out. Tim Dettmer announced that and then somebody looked it up for me yesterday and it's like between that announcement it took us seven days to get that integrated into Axolotl, right? Which is like, it's not. I wouldn't say it's really fast, but in a manner that, is in a, a reusable framework, I think it was quite the accomplishment then.[00:06:33] And so we started, picking up traction with people there. And then it's just been building models, and then just iterating what my needs are. So, yeah. Excellent. Yeah. I[00:06:44] Alex Volkov: want to ask, for folks who are listening who never heard of Axolotl, now do you describe how you got there?[00:06:49] Can you, how do you summarize this for folks who maybe haven't fine tuned anything. They know about open source LLM exists, they maybe know like LLAML, what's XLR for somebody who doesn't know. I've never heard of a data set curation[00:07:01] Wing Lian: creation before. We sort of have to take a step back and understand that, when you've got these language models, you have what I think most people refer to as like base models, also known as like foundational models, right?[00:07:15] Where some benefactor, whether it's Meta or Mistral or whoever, has gone and spent all this money. To train these models on huge corpuses of text, right? And these, these corpuses, they're generally good across lots of different things, but they're really good at just saying, talking on and on and on, but they're not good at, following instructions or having chats or anything like that.[00:07:40] So, when you think about fine tuning, it's like Saying, all right, we have this really sort of good generalized, text completion thing, and I want to turn it into something that I can talk to or have, follow instructions. So, I think fine tuning is probably best defined in like that.[00:07:58] swyx: Okay, got it.[00:07:59] And we actually[00:08:01] What is finetuning?[00:08:01] swyx: Do want to make sure that we have like an overall introduction to fine tuning for people because again like trying to make sure that we bring everyone along in this, in this journey. We already went into Loras and QLoras without explaining what[00:08:12] Wing Lian: they are. Oh yes, yes, sorry.[00:08:14] swyx: And so I will put things in my words and you can correct me as, as, as my I'll be the village idiot here.[00:08:21] So, so fine tuning is basically sort of grabbing an open source model off the shelf, and then basically doing further training on it with a custom dataset of your own. Primarily, people use it, think about it as fine tuning for JSON output, or fine tuning for a style of response. Let's say you wanted to tell jokes, or be funny, or be short, or whatever.[00:08:43] Just the open source AI community has really fine tuned in all sorts of different manner. I think we'll go over those those things now. Let's go over those things now, and then we'll talk about fine tuning methods.[00:08:52] Open Source Model Zoo[00:08:52] swyx: So there's a universe of people who fine tune stuff. Yesterday in your slides, you had, I'll just list some of these and then we'll maybe go through some of them, right?[00:08:59] So Technium is personally leading Open Hermes, which is I think the sort of premier model out of the news. news community. There's OpenOrca, which you had a hand in. News, the news research itself also has Capybara and Puffin and all the others. There's Pygmalion, which I've never messed with.[00:09:14] Eric Hartford, I am aware of his Uncensored Models and his Samantha Models. Disco Research with Disco LM. And then you personally have done Manticore, Minotaur, Jackalope, and Hippogriff. What should people know about all these names? Being part of AI Twitter is seeing all these things and going dude, I'm being DDoS'ed by all these things and I don't know how different they are.[00:09:32] What should people know? Yeah, so[00:09:34] Wing Lian: I think on a lot of these models, generally, we like to think of those as sort of general models, so If you think about it, what is GPT 4, what is Chad GPT? It's a good general model, and then, One of the services I think that OpenAI offers is like these fine tunings where you're a business and you have very specific business use cases and you might fine tune for that use case.[00:10:00] All of these models are really just general use case that you can then go and maybe Fine tune another lore over it for your use cases, but they tend to be good. With good being relative, it's open source. Open source AI is still sort of is infancy. So, good is, it's pretty reasonable.[00:10:18] It's probably still better than most, high schoolers at answering questions and being able to like figure things out and, and reasoning skills and math and those sorts of things, right?[00:10:27] swyx: And also as measured on the Hugging[00:10:29] Wing Lian: Face leaderboard. Yes, well, that's like a whole other discussion, right, there's a whole other, group of people who, and I, I mostly agree with them that, benchmarks can be, are pretty bogus these days, LM says, I think they published something recently where, even if you think the dataset's not contaminated, you can go and, find contamination And maybe we should step back and say what contamination is, right?[00:10:53] Benchmarks and Contamination[00:10:53] Wing Lian: So we have all of these data, when you go and do these benchmarks, there's a specific data set where there are these questions and usually it's multiple choice. And what can happen is, well, sometimes someone It puts the question, maybe maliciously, maybe accidentally, into the training dataset, and now the, the, your model knows how to answer the test questions really well, but it doesn't, it hasn't generalized the ability to actually do that[00:11:20] Alex Volkov: right.[00:11:21] We've seen some folks competitively announce models that are like the best at that leaderboard, but then it's, it's quite obvious that, In open source? Yeah, and in that leaderboard, for Hugging Face specific, I don't know if LMCs, if that had suffered, but we, there's been some models that seem to have been competitively trained and some leakage happened into their,[00:11:41] swyx: like, supposal.[00:11:43] I understand, once there's been a credible assertion, Hugging Face actually does take them down, right? Yeah, yeah,[00:11:48] Alex Volkov: which is really hard to know, right?[00:11:50] swyx: It's really hard to know, sometimes it's like a pure accident,[00:11:52] Alex Volkov: it's oh, oops. You're going through a mixer. I think, a responsible So acknowledgement, that this kind of happened to you is also important.[00:11:58] I saw LDJ from news research can acknowledge that. Because many of these datasets are collections of other datasets. There's a bunch of people are baking, basically. It's alchemy. Right. And so sometimes you don't know. Sometimes you pull an open source dataset and they announce, oh, you know what, actually, the MMLU benchmark which we used to Specifically identify models that did go into this data set, that then went into that data set.[00:12:22] So sometimes it's actually an accident and folks take it down. But I've seen some competitive folks who want to put their name out there because people are starting to notice which is the top[00:12:30] swyx: model. For those who want a fun take on this so the file one dataset. FindOne model from Microsoft was accused of being contaminated.[00:12:37] And I saw this joke paper that was fantastic. It was called, training on the test set is all you need. It's a super small model that just memorizes everything. It was fantastic. So yeah, contamination, I think we've actually covered it in a previous episode before. So we're good. But again, I want to give people a map into the open source AI model, the universe.[00:12:57] And Alex, you can also jump in here because you guys have spent a lot more time with them than I have. So, what should people know about Technium? What should people know about Noose? And then we can go down the list. Yeah,[00:13:05] Wing Lian: I think so. I think if we start with, Technium. When you talk to him, he's gonna say, I think, I think his response is that he wants to build GP4 on his laptop, right?[00:13:14] So, very, very good at building general models. I think with Noose, Noose Research, they're looking at more, sort of, More, more research focused things, like their Yarn models, I don't, I don't, they didn't actually train their, they have their own trainer for their Yarn models, but So they did not use Xlato for that one?[00:13:30] They didn't use that, but like Is that, you don't have support for it? I think we do support Yarn, I think, I'd have to double check that answer. Yeah, I'm just kind of curious what you can and cannot support, and Yeah, I mean, Yarn is supportable, it's basically, I think it's just replacing, I think, the rope part of that, so Yeah, not, not a big deal.[00:13:48] Yeah, it's not a big deal, it's just I haven't gotten to it, not enough people have asked, I think a lot of people have asked for other things, so it's just, squeaky wheel, right? I think at the end of the day, people are like building these data sets and I think if you sort of map things chronologically, these make more sense because it's like, how do we incrementally improve all of these models?[00:14:07] So a lot of these models are just incremental improvements over the last thing, right? Whether it is sort of through methods of how do we, how did we curate the data set? How did we improve the quality of the data set? So, you maybe LDJ talked about it right on I think for, for Capybara and Puffin, like how those, those were very specific dataset curation techniques that he works on.[00:14:29] The Case for Open Source AI[00:14:29] Alex Volkov: So there's, folks are doing this for dataset curation. Folks are doing this for skillset building as well. Definitely people understand that open source is like very important, especially after the, the, the, the, the march, the debacle, the OpenAI weekend that we all had. And people started noticing that even after developer day in OpenAI, the APIs went out.[00:14:48] And then after that, the whole leadership of the company is swiftly changed and people, there was worries about, you know. How can people continue building AI products based on these like shaky grounds that turned attention definitely to Technium at least in open RMS I started seeing this more and more on Twitter, but also other models and many companies They're gonna start with open AI just to get there quick, and then they they think about okay Maybe I don't want to share my knowledge.[00:15:13] Maybe I don't want to sign up for Microsoft. Maybe they will change their terms and conditions so What else is out there? They turned to other companies. Up until yesterday, Google was nowhere to be found. We've talked about Gemini a little bit before in a previous And you can tune in[00:15:26] swyx: to[00:15:26] Alex Volkov: Thursday Eye.[00:15:26] Yeah, you can tune in to Thursday Eye. We covered the Gemini release a little bit. And but many are turning into the open source community and seeing that Meta released and continues to release and commit to open source AI. Mistral came out and the model is way smaller than LLAMA and performs Significantly better.[00:15:43] People play with OpenRMS, which is currently techniums based, news researched, sourced, axolotl trained OpenRMS, I assume, right? And then they play with this and they see that, okay, this is like GPT 3. 5 quality. We had GPT 4. 5 birthday just a week ago. A week ago, a year ago, a week ago, we never, interacted with these models of this caliber.[00:16:04] And now there's one open source, one that's on my laptop, completely offline, that, I can continue improving for my use cases. So enterprises, companies are also noticing this. And the open source community folks are building the skill set, not only the data sets. They're building the actual kind of, here's how we're going to do this, with Axelotl, with these data sets.[00:16:21] The curation pieces. Now. Interesting. There's like recipes of curation. The actual model training is kind of a competitive thing where people go and compete on these leaderboards that we talked about, the LMC arena, and that recently added open air and recently added open chat and a bunch of other stuff that are super cool.[00:16:37] The hug and face open source leaderboard. And so there's a competitive aspect to this. There's the open source. Aspect to this, like Technium says, I want GPT 4 on my laptop. There's the, let me build a skill set that potentially turns into a company, like we saw with Noose. Noose just, started organizing, a bunch of people on Discord, and suddenly, they're announcing their company.[00:16:54] It's happening across all these modalities, and suddenly all these people who saw these green pastures and a fairly quick way to, hey, here's a cool online community I can, start doing cool stuff with. You mentioned the same in the beginning, right? Like, after your accident, what's cool, let me try this out.[00:17:08] Suddenly I start noticing that there's a significant movement of interest in enterprising companies into these areas. And, this skill set, these data sets, and this community is now very Very important, important enough to create an event which pulls in Andrei Karpathy from OpenAI to come and see what's new Jeremy Howard, like the event that we just talked about, people are flying over and this is just a meetup.[00:17:28] So, definitely, the community is buzzing right now and I think Axelot is a big piece as well.[00:17:34] Orca and OpenOrca[00:17:34] Wing Lian: Cool. Maybe we can talk about like Orca real quick, Orca, OpenOrca rather, I think there was a lot of buzz when, the first Orca paper came out. And just briefly, what is Orca? Yeah, Orca was basically having traces of like chain of thought reasoning, right?[00:17:48] So they go and they, they distill sort of GPT 4. They take, they take a sampling of data from the Flan dataset. Maybe we can like add some show notes in the Flan dataset. Yeah, but we've covered it. Okay, cool. Use GPT 4 to say, all right, explain this in a step by step reasoning, right?[00:18:06] And then you take that and you, they train the model and it showed, very good improvements across a lot of benchmarks. So OpenOrca was sort of the open reproduction of that since Microsoft Research never released that particular data set. And going back to sort of the Hugging Face leaderboard thing, those models did really well.[00:18:23] And then I think, so sort of the follow up to that was SlimOrca, right? I think Going into and building the OpenOrca dataset, we never really went in and, validated the actual answers that GPT 4 gave us, so what we did was one from OpenChat actually cross referenced the original Flan, the original Flan response, the human responses, the correct answers with the dataset, and then I went and took it and sent all of, both of them to GPT 4 and said, is this answer mostly correct, right?[00:18:54] Yeah. And then we were able to filter the dataset from, At least of the GPT 4 only answers from like 800, 000 to like 500, 000 answers or rows and then, and then retrain the model and it had the same performance as the original model to within I think, 0. 1 percent here about, and 30 percent less data.[00:19:13] So, yeah. Okay.[00:19:15] swyx: Interesting. So, I mean, there's, there's so much there that I want to highlight, but yeah. Orca is interesting. I do want people to know about it. Putting chain of thought into the data set like it's just makes a ton of sense one thing I think it would be helpful for people to scope thing these things out is how much data are we talking about when when you When people are fine tuning and then how much time or resources or money does it take to train to fine[00:19:36] Wing Lian: tune?[00:19:37] Yeah, so I think there's a little bit of overlap there with sort of like fine tuning techniques, but let's say Orca and I think even Hermes, they're both relatively large data sets like 10 billion tokens. Yeah. So large data sets being or the original Orca was, or the original open Orca was 800,000 rows.[00:19:55] I believe it was somewhere in the ballpark of like a gigabyte of data, of gigabyte, of text data. And I, I don't. I believe, Hermes was, is like a quarter million rows of data, I don't know the actual byte size on that particular one. So, going and training a, let's, let's say everybody's training 7 billion Mistral right now, right?[00:20:15] So, to tri I, I believe to fine tune 7 billion Mistral on, let's say, 8 A6000s, which have 48 gigabytes of VRAM, I believe, It takes about 40 hours, so 40, and then that's, depending on where you get your compute, 40 times 6, so it's like 500 to fine tune that model, so, and, and that's assuming you get it right the first time, right?[00:20:44] So, you know.[00:20:45] swyx: Is, is that something that X. Lotto handles, like, getting it right the first[00:20:48] Wing Lian: time? If you talk to anybody, it's like you've probably tried at least three or four runs or experiments to like find the right hyperparameters. And after a while you sort of have a feel for like which, where you need your hyperparameters to be.[00:21:04] Usually you might do like a partial training run, do some benchmark. So I guess for Al Farouk, whether you're going by his. This is Jeremy, he's, his actual name, or his twitter handle. He released the Dharma dataset, which is basically a subset of all the benchmarks. And Axolotl actually supports, you know taking that subset and then just running many benchmarks across your model every time you're doing an evaluation so you can sort of like see sort of relative it's not going to be the actual benchmark score, but you can get ideas alright, is this benchmark improving, is this benchmark decreasing, based on, you know Wait,[00:21:39] swyx: why don't you run the full benchmark?[00:21:41] What, what, what The[00:21:42] Wing Lian: full benchmarks take Take a long time. Significant, yeah, significant amount of time. Yeah. And Okay, so that's like[00:21:48] swyx: mini MMLU. Yeah. Like,[00:21:49] Wing Lian: mini BigBench or whatever. Yep, exactly.[00:21:51] Alex Volkov: It's really cool. We, when I joined Web2Masters just recently, and one of the things that I try to do is hey I'm not, I'm a software engineer by trade, I don't have an MLE background, But I joined a company that does primarily MLE, and I wanted to learn from the community, Because a lot of the open source community, they use weights and biases, And the benchmark that you said that Pharrell did, remind me of the name, sorry.[00:22:13] Dharma? Dharma, yeah, yeah. So Luigi showed me how Dharma shows inside the dashboard. In Wi and Biases dashboard and so you can actually kinda see the trending run and then you can see per each kind of iteration or, or epoch or you can see the model improving trending so you can on top of everything else.[00:22:29] The wi and biases gives like hyper parameter tracking, which like you, you started with common line and that's really hard to like remember. Also the Dharma data set, like the quick, the mini orca mini, you mini many different things. It's pretty cool to like visualize them as well. And I, I heard that he's working on a new version of, of Dharma, so Dharma 2, et cetera.[00:22:47] So hopefully, hopefully we'll see that soon, but definitely it's hard, right? You start this training around, it said like 40, 50 hours. Sometimes, sometimes it's like your SSHing into this machine. You, you start a process, you send it with God and you just go about your day, collecting data sets, and then you have to return.[00:23:04] And the whole process of instrumentation of this is still a little bit like squeaky but definitely. Tuning performance, or like grabbing performance in the middle of this, like with Dharma and some other tools, is very helpful to know that you're not wasting precious resources going somewhere you shouldn't go.[00:23:21] Yeah.[00:23:22] swyx: Yeah. Very cool. Maybe I'll, I'll, before we go into like sort of more details on fine tuning stuff, I just wanted to round out the rest of the Excel autoverse. There's, there's still Eric Hartford stuff. I don't know if you want to talk about Pygmalion, Disco, anything that you know about[00:23:35] Wing Lian: those, those things.[00:23:36] DiscoLM and Model Stacking[00:23:36] Wing Lian: Yeah, I think like one of the, definitely one of the more interesting ones was like the Disco 120b, right? Yeah, I know nothing about it. Yeah. So, so. Alpen from Pygmalion AI, right, so they, so Pygmalion is a sort of a, it's, it's, they have their own community, a lot of it is based around, roleplay models, those sorts of things, and Alpen, like, put together, merged together Llama270B, so, and Alpen, like, put together, merged together Llama270B, so, I don't remember how he stacked them together, whether he merged the layers in between. There's a whole, there's a whole toolkit for that by Charles Goddard, where you can like take a single model and like stack them together or multiple models merge.[00:24:18] That's like a whole other talk and a whole other tool set, but was able to create this 120. Billion parameter model out of a LAMA two 70 B. And then I believe the, yeah, disco is a fine tune of, of the, the, the sort of the base one 20 B is, I believe Goliath one 20 B. So, and, and what are the[00:24:37] swyx: headline results that people should know about[00:24:39] Wing Lian: disco?[00:24:39] I think for the headline results, I, I've, I haven't played with it personally because it's. It's a very large model and there's a lot of GPU, right? But, like, from what I've heard anecdotally, it performs really well. The responses are very good. Even with, like, just, even the base model is a lot better than, Llama70b.[00:24:57] So, and we, I think generally everybody's like, we would all love to fine tune Llama70b, but it's just, it's so much, it's so much memory, so much compute, right?[00:25:07] Datasets and Evals over Models[00:25:07] Wing Lian: I[00:25:07] Alex Volkov: want to touch on this point because the interesting thing That comes up out of being in this ecosphere and being friends with open source folks, tracking week to week state of the art performance on different models.[00:25:19] First of all, a lot of the stuff that the folks do a couple of weeks ago, and then something like Mistral comes out, and a lot of the stuff back then, Doesn't technically make sense anymore. Like the artifacts of that work, the actual artifacts, they don't no longer make sense. They're like lower on the on, on the hug and face leaderboard or lower on LM CS leaderboard.[00:25:36] But some of the techniques that people use, definitely the datasets. The datasets keep traveling, right? So open airmen, for example, is the dataset. The tum cleaned up for only. Open sourceable data that previously was just Hermes. And that, it was previously used to train Lama. And then once Mistral came out, it was used to train Mistral.[00:25:54] And then it became significantly better on the 7b base Mistral. So the data sets keep traveling, keep getting better a little bit here and there. And so the techniques improve as well. It looks like both things are simultaneously true. The artifacts of a month and a half ago. The, the actual models themselves, it's great the hug and face has them, because not every company can keep up with the next weeks', oh, I, I'll install this model instead, sell this model instead.[00:26:19] But the, the techniques and the, the dataset keep improving as we go further, and I think that's really cool. However, the outcome of this is that for a long time. For many, many people, including us, that we do this every week. We literally talk with people who release these models every week. It's really hard to know.[00:26:36] So, there's a few aspects of this. One, I think, like you said, the bigger model, the 70B models, you actually have to have somebody like Perplexity, for example, giving you access to the 70B really fast. Or you have to, like, Actually, find some compute, and it's expensive, especially for the bigger models. For example Falcon 180B came out, like the hugest open source model.[00:26:56] How do you evaluate this if you can't run it? Nobody liked it. It's really, so first of all, nobody liked it, but secondly, only the people who were able to find compute enough to run inference on this, they only had like, I can't run this on my laptop, and so that's why it's much easier, something like OpenRMS 7 to be, 7B, it's much easier, because you can run this on your MacBook.[00:27:14] It's much easier to evaluate. It's much easier to figure out the vibes, right? Everybody talks about the vibes as an evaluation check. If you're plugged in enough, if you follow the right people, if they say pretty much the same things all independently, then you run into a problem of whether they're repeating, and their stochastic parents are repeating the same thing, or they actually evaluated themselves.[00:27:31] Yeah, you never know. But, you never know, but like, I think on a large enough scale on Twitter, you start getting the feel. And we all know that like, OpenRMS is one of the top performing models, benchmarks, but also vibes. And I just wanted to highlight this vibes checks thing because you can have the benchmarks, you can have the evaluations, they potentially have contamination in them, potentially they not necessarily tell you the whole story because some models are good on benchmarks, but then you talk to them, they're not super helpful.[00:28:00] And I think it's a combination of the benchmarks, the leaderboards, the chatbot, because LMSys, remember, their ranking is not only based on benchmarks, it's also people playing with their arena stuff. People actually like humans, like, get two answers. I think they completely ignore benchmarks. Yeah, and then They only do ELO.[00:28:18] Oh, they do ELO completely, right? So that, for example, is just like people playing with both models and say, Hey, I prefer this one, I prefer that one. But also there's like some selection bias. The type of people who will go to LMCs to play with the models, they're a little bit specific in terms of like who they are.[00:28:33] It's very interesting. There's so many models. People are doing this in this way, that way. Some people are doing this for academic rigor only to test out new ideas. Some people are actually doing this like the Intel fine tunes of Mistral. Intel wanted to come out and show that their hardware approach is possible, Mistral, etc.[00:28:51] And it's really hard to know, like, what to pick, what to use. And especially on the bigger models, like you said, like the Llama 70B, the Falcon 180B. It's really because, like, who has the compute to validate those? So I would mention that, like, use with caution. Like, go and research and see if the biggest model that just released was actually worth the tokens and the money you spend on it.[00:29:12] To try and, if you're a business, to integrate it.[00:29:15] Distilling from GPT4[00:29:15] swyx: Since you said use of caution, I'll bring in one issue that has always been in the back of my mind whenever I look at the entire universe of open source AI models, which is that 95 percent of the data is derived from GPC 4, correct?[00:29:30] Which technically you can't use for commercial licenses,[00:29:34] Wing Lian: right?[00:29:35] swyx: What is the community's stance on this kind of stuff?[00:29:40] Wing Lian: I think from the community stance, like I feel like a lot of us are just experimenting, so for us, it's like, we're not going and building a product that we're trying to sell, right?[00:29:49] We're just building a product because we think it's interesting and we want to use it in our day to day lives, whether or not we try and integrate it. Personal use, yeah. Yeah, personal use, so like, as long as we're not selling it, yeah, it's fine. But[00:30:01] swyx: like, I as a company cannot just take OpenHermes and start serving[00:30:05] Alex Volkov: it and make money on it.[00:30:06] OpenHermes you can. Because the opening of OpenHermes, I think, is a clean up. That did after the regular Hermes, please folks, check your licenses before you listen to podcasts and say, Hey, I will tell you though, you could say the same thing about OpenAI. You could say the same thing kind of makes sense, where OpenAI or StabilityAI trains their diffusion model on a bunch of pictures on the internet, and then the court kind of doesn't strike down Sarah Silverman, I think, or somebody else, who came and said, hey, this has my work in it, because of the way how it processes, and the model eventually builds this knowledge into the model, and then it doesn't actually reproduce one to one what happened in the dataset.[00:30:45] You could claim the same thing for open source. Like, we're using And by we, I mean the, the open source community that I like happily report on uses GPT 4 to rank, for example, which is the better answer you, you, that's how you build one, one type of data set, right? Or DPO or something like this, you, you basically generate data set of like a question and four answers, for example, and then you go to GPT 4 and say, Hey, smartest model in the world right now, up to Gemini Ultra, that we should mention as well.[00:31:11] Which one of those choices is better? But the choices themselves are not necessarily written with GPT 4. Some of them may be, so there's like full syntactic datasets. But there's also, datasets are just ranked with GPT 4. But they're actually generated with a sillier model, or like the less important model.[00:31:25] The lines are very blurry as to what type of stuff is possible or not possible. And again, when you use this model that's up on Hug Face, the license says you can use this. OpenAI is not going to come after you, the user. If anything, OpenAI will try to say, hey, let's prevent this, this type of thing happening, and the brain, but I honestly don't think that they could know even, not that it makes it okay, it's just like, They also kind of do this with the Internet's archive, and also, I think that some of it is for use.[00:31:55] You use models to help you augment tasks, which is what GPT 4 lets you do.[00:32:00] swyx: Yeah, the worst thing that OpenAI can do is just kick you off OpenAI. That's because it's only enforced in the terms of service.[00:32:05] Alex Volkov: Sure, but just like to make sure, to clarify who they're going to kick out, they could kick out like News, for example, if news are abusing their service, a user of the open source, fully Apache 2 open source, for example, They won't get kicked out if they use both, just because they use both.[00:32:22] I don't believe so. I don't think OpenAI has a claim for that.[00:32:25] swyx: Well, we're not lawyers, but I just want to mention it for people to know it's an issue.[00:32:30] Wing Lian: And one of the things, like, I talked to someone recently, and I think that they also are like interested in it, but also to the point of like, right, if I use a model trained on data, using GPT for data, But I use that model to then regenerate new data.[00:32:46] Is that model, is that data okay? So like you start going down this whole rabbit hole. So yeah. All right.[00:32:53] swyx: Fantastic. Cool. Well, I think that's roughly highlights most of the open source universe. You also have your own models. Do you want to shout out any one of them? Yeah.[00:33:01] Wing Lian: I mean, I think like, I think Early on, Manicore got a lot of love.[00:33:04] I think it was mostly popular in, like, the roleplay communities. It was, it tended to be pretty truthful. It tended to be, like, have relatively good answers, depending on who you ask, right? But, I think for me, it was just, Releasing models was a way to try and, like, continue to build out the product, figure out what I needed to put into the product, how do I make it faster, and, if you've got to, like, go and debug your product, you may as well have it do something useful.[00:33:29] Awesome. So, yeah.[00:33:31] Finetuning - LoRA, QLoRA, ReLoRA, GPTQ[00:33:31] swyx: Okay, and then maybe we'll talk about just fine tuning techniques. So this is going to be a little bit more technical than just talking about model names and datasets. So we started off talking about LoRa, QLoRa. I just learned from your readme there's ReLoRa. Which I've never heard about.[00:33:45] Could you maybe talk about, like, just parameter efficient fine tuning that whole, that[00:33:50] Wing Lian: whole journey, like, what people should know. Yeah, so with parameter efficient fine tuning, I think the popular ones, again, being, let's, we'll start with lore, right? So, usually what you do is you freeze all the layers on your base, on the base model, and then you, at the same time, you sort of introduce additional Oh, this is tight.[00:34:08] No. You introduce, another set of layers over it, and then you train those, and it is done in a way that is mathematically possible, particularly with LORs that you can, then you, you, When you, when you train the model, you, you run your inputs through the base model, whose weights are frozen, but you, then you also run it through the additional weights, and then at the end you combine the weights, and then, and then, or you combine the weights to get your outputs, and then at the end, and when you're done training, you're left with this other set of weights, right, that are completely independent, and And then from that, what you can do is, some person smarter than I figured out, well, oh, they've done it in such a way that now I can merge these weights back into the original model without changing the architecture of the model, right?[00:35:03] So, so, that tends to be, like, the go to, and You're training much fewer parameters so that when you do that, yes, you still need to have all of the original weights, but you have a smaller gradient, you have a smaller optimizer state, and you're just training less weights, so you can tend to train those models on, like, much smaller GPUs.[00:35:27] swyx: Yeah. And it's roughly like, what I've seen, what I've seen out there is roughly like 1 percent the number of parameters that you're trading. Yeah, that sounds about right. Which is that much cheaper. So Axelotl supports full fine tune, LoRa, QLoRa,[00:35:40] Wing Lian: Q. Yes. So, so QLoRa is, is very similar to LoRa. The paper was, if I remember correctly, the paper was Rather, traditionally, most people who did Loras were, were, they were quant, they were putting the model weights in 8 bit, and then fine tune, parameter efficient fine tuning over the Lora weights, and then with QLora, they were quantizing all of those, they were then quantizing the weights down to 4 bit, right, and then I believe they were also training on all of the linear layers in the model.[00:36:15] And then with ReLore, that was an interesting paper, and then, I think, like, it got implemented. Some people in the community tried it, tried it out, and it showed that it didn't really have the impact that the paper indicated that it would. And from what I was told recently, that they re I guess they re released something for Relora, like, a few weeks ago, and that it's possibly better.[00:36:44] I personally haven't had the time. What was the[00:36:46] swyx: main difference,[00:36:47] Wing Lian: apart from quantization? I don't know. Okay. What was the main difference, sorry?[00:36:49] swyx: Apart from quantization, right? Like,[00:36:50] Wing Lian: Qlora's thing was, like, we'll just drop off some bits. With Relora, what they did was, you would go through, you would define some number of steps that you would train, like, your Lora with, or your Qlora.[00:37:01] Like, you could do Like, ReqLore, if you really wanted to, you would, you would train your LoRa for some number of steps, And then you would merge those weights into your base model, and then you would start over. So by starting, so, then by starting over, The optimizer has to find, like, sort of, re optimize again, and find what's the best direction to move in, and then do it all again, and then merge it in, do it all again, and theoretically, according to the paper, doing ReLore, you can do parameter efficient fine tuning, but still have sort of, like, the performance gains of doing a full fine tuning, so.[00:37:38] swyx: Yeah, and[00:37:39] Wing Lian: GPTQ? And GPTQ, so it's, I think with GPTQ, it's very similar to, more similar to QLore, where you're, it's mostly a quantization of the weights down to like 4 bit, where GPTQ is a very, is a specific methodology or implementation of quantization, so. Got it.[00:37:57] Alex Volkov: Wang, for, for folks who use Axolotl, your users, some people who maybe, Want to try it out?[00:38:03] And do they need to know the differences? Do they need to know the implementation details of QLora versus ReLora? Or is it okay for them to just know that Axolotl is the place that already integrated them? And if that's true, if that's all they need to know, how do they choose which method to use? Yeah,[00:38:22] Wing Lian: so I think like, I think most people aren't going to be using ReLora.[00:38:25] I think most people are going to be using either Lora or QLora. And I think they should have it. They should have an understanding of why they might want to use one over the other. Most people will say that with Qlora, the quality of the final model is not quite as good as like if you were to do a LoRa or a full fine tune, right?[00:38:44] Just because, you've quantized these down, so your accuracy is probably a little off, and so that by the time you've done the Qlora, you're not moving the weights how you would on a full fine tune with the full parameter weights.[00:38:56] Interesting.[00:38:57] swyx: Okay, cool. For people who are more interested, obviously, read the papers. I just wanted to give people, like, a high level overview of what these things are. And you've done people a service by making it easy for people to try it out. I'm going to, I'm going to also ask a question which I know to be wrong, but I'm curious because I get asked this all the time.[00:39:15] What is the difference between all these kinds of fine tunes[00:39:17] Wing Lian: and RLHF? Okay, between all of these sorts of fine tunes and RLHF. So all of these sorts of fine tunes are based, are, ideally, this, they are taking knowledge that the base model already knows about, and presenting it in a way to the model that you're having the model answer like, Use what it already knows to sort of answer in a particular way, whether it's, you're extracting general knowledge, a particular task, right?[00:39:44] Instruct, tune, chat, those sorts of things. And then generally with RLHF, so what is, let's go back, what is it? Reinforcement Learning with Human Feedback. So if we start with the human feedback part, What you're doing is you generally have, you have like a given prompt and then you, maybe you have one, maybe you have two, I think, like if you look at with Starling, you have like up to what, seven different, seven different possible responses, and you're sort of ranking those responses on, on some sort of metric, right, whether the metric is how much I, I might like that answer versus or I think with like starling is like how how how helpful was the answer how accurate was the answer how toxic was the answer those sorts of things on some sort of scale right and then using that to go back and like sort of Take a model and nudge it in the direction of giving that feedback, to be able to answer questions based on those preferences.[00:40:42] swyx: Yeah, so you can apply, and is it commutative? Can you apply fine tuning after and onto an RLHF model? Or should the RLHF apply, come in afterwards,[00:40:54] Wing Lian: after the fine tune? Um, I, yeah, I don't know that there's There's been enough research for one way or another, like, I don't know.[00:41:02] That's a question that's been asked on Discord. Yeah, like, I definitely would say I don't know the answer. Go and try it and report back to me and let me know so I can answer for the next guy.[00:41:10] swyx: It's shocking how much is still unknown about all these things. Well, I mean, that's what research is for, right?[00:41:16] Wing Lian: So actually I, I think I saw on the top of a leaderboard, it was a, it was a mytral base model, and they didn't actually fine tune it. They, or they, they just did RLH, they did like an RLHF fine tune on it using like, I don't, I don't recall which dataset, but it was like, and it benchmarked really well.[00:41:37] But yeah, you'd have to go and look at it. But, so it is interesting, like going back to that, it's like. Traditionally, most people will fine tune the model and then do like a DPO, PPO, some sort of reinforcement learning over that, but that particular model was, it seemed like they skipped like the supervised fine tuning or Scott.[00:41:55] Axolotl vs HF Transformers[00:41:55] swyx: Cool. One thing I did also want to comment about is the overall, like, landscape, competitive landscape, I don't know. Hugging Face Transformers, I think, has a PFT module.[00:42:05] Wing Lian: Yeah, yeah, the PEFT, the Parameter Efficient Fine Tuning, yep. Is that a competitor to you? No, no, so we actually use it. We're just a wrapper over sort of, sort of the HuggingFace stuff.[00:42:15] So, so that is their own sort of module where They have, taken the responsibility or yeah, the responsibility of like where you're doing these parameter efficient fine tuning methods and just sort of like, it is in that particular package where transformers is mostly responsible for sort of like the modeling code and, and the trainer, right.[00:42:35] And then sort of, there's an integration between the two and, there's like a variety of other fine tuning packages, I think like TRL, TRLX, that's the stability AI one. Yeah, I think TRL likes the stability, yeah, Carper, and TRL is a hugging face trainer. Even that one's just another wrapper over, over the transformers library and the path library, right?[00:43:00] But what we do is we have taken sort of those, yes, we've We also use that, but we also have more validation, right? So, there are some of us who have done enough fine tunes where like, Oh, this and this just don't go together, right? But most people don't know that, so like Example?[00:43:19] Like, people want to One and one doesn't go together. I don't have an example offhand, but if you turn this knob and this knob, right? You would think, all right, maybe this will work, but you don't know until you try. And then by the time you find out it doesn't work, it's like maybe five minutes later, it's failed.[00:43:34] It's failed in the middle of training or it's failed during the evaluation step. And you're like, ah, so we've, we've added a lot of, we've added a lot more validation in it. So that like, when you've, you've created your configuration, you run it through and now you say. The validation code says this is probably not right or probably not what you don't, not what you want.[00:43:52] So are you like a, you[00:43:53] swyx: do some linting of your YAML file?[00:43:56] Wing Lian: There, I guess you could call it linting, it's sort of like Is there a set of rules out[00:44:00] swyx: there somewhere? Yeah, there's a set of rules in there. That's amazing, you should write documentation like This rule is because, this user at this time, like, ran into this bug and that's what we invested in.[00:44:10] It's like a good collection[00:44:11] Wing Lian: of knowledge. Yeah, it is, and I guess like, if you really wanted to, like, figure it out, I guess you could, like, git blame everything, and But, yeah, it's, so, I think that's always a useful thing, it's like Because people want to experiment but they don't, people will get frustrated when you've experiment, you're experimenting and it breaks and you don't know why or you know why and you've just gone down the rabbit hole, right?[00:44:37] So, so I think that's one of the big features that's, that I think I find important because it's It prevents you from doing things you probably shouldn't have, and it, and sometimes we will let you do those things, but we'll try and warn, warn you that you've done that.[00:44:50] I[00:44:51] Alex Volkov: have a follow up question on this, actually, because yesterday we hung out to this open source event, and I spent time by you a couple times, like when people told you, oh, XLR, I use XLR, it's super cool, and then the first thing you asked is, like, immediately, like, what can we improve?[00:45:04] And yes, from multiple folks, and I think we talked about this a little bit, where there's It's a developer tool. It's like a machine learning slash developer tool. Your purpose in this is to help and keep people, as much as possible, like, Hey, here's the best set of things that you can use right now. The bear libraries are, or the bear trainer, for example, is a bear trainer.[00:45:28] And also, maybe we should talk about how fast you're implementing these things. So you mentioned the first implementation took a week or so. Now there's a core maintainer group, right? There's like, features are landing, like Qlora, for example. Neftune, I don't know if that's one example of something that people potentially said that it's going to be cool, and then eventually, like, one of those things that didn't really shake out, like, people quickly tested this out.[00:45:48] So, there's a ton of Wait, Neftune is cancelled? I don't know if it's fully canceled, but based on vibes, I heard that it's not that great. So like, but the whole point that I'm trying to make with Neftune as well is that being existing in the community of like XLR or like, I don't know, even following the, the GitHub options or following the Discord, it's a fairly good way to like, learn these, Kind of gut feelings that you just, you just said, right?[00:46:14] Like where this, maybe this knob, that knob doesn't work. Some of these are not written down. Some of these are like tribal knowledge that passes from place to place. Axel is like a great collection of many of them. And so, do you get That back also from community of folks who just use, like, how do you know who uses this?[00:46:30] I think that's still an issue, like, knowing if they trained with XLR or should they add this to things? Talk about, how do you get feedback and how else you should get feedback?[00:46:38] Wing Lian: Yeah, I mean, most of the feedback comes from the Discord, so people come in and , they don't get a training running, they run into, like, obscure errors or, errors that That's a lot of things that maybe, maybe as a product we could catch, but like, there's a lot of things that at some point we need to go and do and it's just on the list somewhere.[00:46:58] Right that's why when people come up, I'm like, what, what were your pain points? Because like, as a developer tool, if you're not happy with it, or you come in and in the first, Takes you 30 minutes and you're still not happy. You leave the tool and you may, you might move on maybe to a better tool, maybe to, one with less frustration, but it may not be as good, right?[00:47:17] So I'm trying to like, figure out, all right, how can I reduce all this frustration? Because like for me, I use it every day for the most part, right? And so I am blind to that, right? Mm-Hmm. . Mm-Hmm. . I just know, I, I go do this, this, and this. It pretty much mostly works, right? But, so I don't have sort of that, alright, that learning curve that other people are seeing and don't understand their pain points.[00:47:40] Yeah,[00:47:40] Alex Volkov: you don't have the The ability to onboard yourself as a new user completely new to the whole paradigm to like get into the doors of like, Oh, no, I don't even know how to like ask about this problem or error.[00:47:53] swyx: Cool. The last few things I wanted to cover was also just the more advanced stuff that you covered yesterday.[00:48:00] 20x efficiency with StackLlama and Multipack[00:48:00] swyx: So I'll just, caution this as like, yeah, this is more advanced. But you mentioned Stackllama and Multipack. What are they[00:48:06] Wing Lian: and what should people know? Yeah, so, so, Stack Llama was, that paper came out, so Stack Llama I think was like, two, two, two separate, two separate concepts that they announced, so the first one was They being hugging face.[00:48:20] Yeah, sorry, yes, they being hugging face, so the first one being sort of like, this idea of packing, like some packing sequences together, so like, if we think about training data, right, your training data is, let's say, to keep the math easy, let's say your training data is 500, We, we, we, we will use the terminology words.[00:48:39] Let's say your training data is 500 words long, and let's say your, your context length, you know how much data your, that your model can accept is like, or that you want feed into your model. It's, let's say, we won't use tokens again, we'll we'll use it is it's 4,000 tokens, right? So if you're training at 4K Con or four 4,000 4K contacts and you're only using 500 of it, you're sitting like with the other 1500.[00:49:05] 3, 500 words that you're not using, right? And typically that's either filled with these PAD tokens, so I think I made the analogy last night that it's like having sort of like a glass here you fill it up with a shot of liquor and then you're and that's your training data and then you just fill it up with more water and those are your PAD tokens and it's just, it doesn't do much, right?[00:49:27] It's still the same thing, but you still have to go through all of that to go through all your training data. And then, so what Stack Llama showed was you could just sort of take your training data, append the next row of training data until you filled that entire 4k context, so in this example, right, with 500 words to 4k, that's 8 rows of training data.[00:49:48] But, the problem with that is, is that with a lot of these transformer models, they're very much relying on attention, right? So, like, if you now have this sequence of words that now, in order for the, the model has seen all of these other words before, right? And then it sees another set of words, another set of words, but it's learning everything in context of all the words that it's seen before.[00:50:13] We haven't corrected the attention for that. And just real quickly, since I said that that paper was two concepts, the other one was, I believe it was like a reinforcement learning, but outside the scope of this. So going from that, I implemented that early on because I was like, Oh, wow, this is really great.[00:50:29] And. Yes, because it saves you a bunch of time, but the trade off is a little bit of accuracy, ultimately, but it still did pretty well. I think when I did Manicore, I think it used sort of that concept from Stack Llama of just sort of appending these sequences together, right? And then sort of the next evolution of that is Multipack, right?[00:50:51] So, there was a separate paper on that, it was, I believe it was referenced, it got referenced in the Orca paper, where you could, you could properly mask those out using like a, I think it was like a lower block triangular attention mask, and then sort of, so, So, there's that. I did try implementing that, manually recreating that mask, but then one from the OpenChat, so he was helping with OpenOrca as well, and he had done an implementation of Multipack, and where he used FlashAttention, so FlashAttention So that was released by TreeDAO, and it was this huge performance gain.[00:51:35] Everybody uses it now, even the Transformers library now, they've taken all of these, like, people are taking all of these models and sort of like, making it compatible with FlashAttention. But in Flash Tension, there is one particular implementation that lets you say, Well, I'm sending you all of these sequences like you would in Stack Llama, But let me send you another, another, Set of information about, this is where this set of sequences is, this is where the second set of sequences is.[00:52:06] So like, if it was like, 500 words long, and you stacked them all together, you would just send it a row of information that was like, 0, 500, 1000, 1500, etc, etc, out to 4000. And it would know, alright, I need to break this up, and then run the forward pass with it. And then it would be able to, and it was much more, much more performant.[00:52:29] And I think you end up seeing like 10x, 20x improvements over sort of, I mean, I think FlashAttention was like a 2x improvement, and then adding that with the Multipack, you start to see like, depending on, how much data you have, up to like a 20x improvement sometimes. 20x. 20x. Wow. Yeah.[00:52:48] And I only know the 20x because I, like, before last night, I was like, I re ran the alpaca, I looked up the alpaca paper because it was like, I just need a frame of reference where somebody did it, and I think they used eight A100s for three hours, and they said it cost them 100. I don't, I don't think eight A100s cost, I don't know how much it costs right now.[00:53:14] But I ended up rerunning it. Usually a dollar an hour, right? Yeah, so eight. The cheapest is like a[00:53:18] Alex Volkov: dollar, a dollar an hour for one.[00:53:20] Wing Lian: Yeah, so that's still like 24, 25. But maybe if you're going on Azure, maybe it's like, maybe it's 100 on Azure. I mean, it used to be more expensive, like, a year ago.[00:53:31] Yeah, and then, so I re ran it with sort of like, I turned on all of the optimizations just to see what it would be. And like, and usually Multipack is the biggest optimization, so Multipack with Flash Detention. And it, I think I spun it up on 8 L40s, and it ran, and I didn't let it run all the way through, I just grabbed the time, the estimated completion time, and it was like 30 minutes, so it would have cost like 4 or 5 to run the entire, like, reproduce the alpaca paper, right?[00:54:00] Which is crazy. It's crazy. 20x,[00:54:02] Alex Volkov: yeah. I want to ask about, like, you said you turned on all the optimization. Is that the yaml file with xlodl, you just go and like check off, like, I want this, I want that? Yeah, yeah,[00:54:10] Wing Lian: so there's like one particular yaml file in there, That, there's one particular YAML file in there that's like, it's under examples, llama2, fft, optimize.[00:54:20] So, I think someone had created one where they just turned, they put in all of the optimizations and turned them on. I mean, it actually, it does run, which is like, sort of surprising sometimes, because sometimes, you optimize this, optimize this, and sometimes they just don't work together, but, yeah.[00:54:36] Just turn the knobs on, and like, fine tuning should really just be that easy, right? I just want to flip the knob and move on with my life and not figure out how to implement it.[00:54:47] Tri Dao and Mamba[00:54:47] Alex Volkov: Specifically, the guy behind FlashAttention came up with something new. You want to talk about this a little bit? You want to briefly cover Mamba?[00:54:53] Yeah, let's talk about Mamba. Let's talk about Mamba. So, what is Mamba?[00:54:57] Wing Lian: Oh, gosh. I

It's that Spooky time of year again, so come join us on another creature feature episode of triple C as Morrison sits down with Cristy Phillips and they go through Ghouls, Mummies and Witches...... And don't forget the rest of the great shows of SGN this spooky season!The CCC merch store is now LIVE! Get you a brand new t-shirt, drinking glass, sticker pack, and way more!! https://somethinggoodnetwork.bigcartel.com/category/the-cryptid-consparacy-cultListen to other shows on the SOMETHING GOOD NETWORK: https://linktr.ee/somethinggoodnetwork Join our Discord: https://discord.gg/Ge53Nu9

Co-hosts Benny Loy and Lindsey Anderson share some of their favorite Halloween movies, as Spokane continues to live up to an unfortunate reputation. Lindsey interviews SGN contributing writer Ian Crowley. Odyssey Youth Movement: https://www.odysseyyouth.org/ RADIO SGN INFO Merch: www.redbubble.com/people/SeattleGayNews/shop Instagram: @radio.sgn Twitter: @radiosgn Music Intro: Meatball by Jesse Spillane Transitions: Night Sun by TRG Banks --- Send in a voice message: https://podcasters.spotify.com/pod/show/seattle-gay-news/message Support this podcast: https://podcasters.spotify.com/pod/show/seattle-gay-news/support

The SGN's annual Travel Issue is out! Hear about the adventures of SGN staff to the far corners of the world, like South Korea, California, and Portland. Our co-hosts Benny and Lindsey are also joined by the paper's talented "social media god" Cameron Martinez for the first installment of a fun new segment. Lindsey interviews author and educator Aidan Key about his upcoming book Trans Children in Today's Schools. Learn more at: genderdiversity.org RADIO SGN INFO Merch: www.redbubble.com/people/SeattleGayNews/shop Instagram: @radio.sgn Twitter: @radiosgn Music Intro: Meatball by Jesse Spillane Transitions: Night Sun by TRG Banks --- Send in a voice message: https://podcasters.spotify.com/pod/show/seattle-gay-news/message Support this podcast: https://podcasters.spotify.com/pod/show/seattle-gay-news/support

It's time for SGN's annual special issue on finance! Co-hosts Lindsey and Benny talk about the nuances of the wealth gap between Queer people and their straight counterparts, and about the veterinary technician shortage, Seattle's overloaded animal shelters, and the many resources out there for struggling pet owners. Lindsey interviews local "dragabond" king Sid Seedy. Sid's Instagram: @sid_seedy RADIO SGN INFO Merch: www.redbubble.com/people/SeattleGayNews/shop Instagram: @radio.sgn Twitter: @radiosgn Music Intro: Meatball by Jesse Spillane Transitions: Night Sun by TRG Banks --- Send in a voice message: https://podcasters.spotify.com/pod/show/seattle-gay-news/message Support this podcast: https://podcasters.spotify.com/pod/show/seattle-gay-news/support

Dai Ballin Talks Performing At Summerfest, Having Money Before Rap, SGN, YNW Melly Mistrial & more https://youtu.be/exK__1tFW98

Guest: Greg AndersonGreg is originally from Myrtle Beach, South Carolina and has been in full-time church-based ministry since 1996 as a small group pastor, most recently at Calvary Chapel Fort Lauderdale. He has been involved with SGN from the early days of its launch, and currently serves as the Southeast Regional Representative for the Small Group Network. Greg is currently on staff with The Fellowship of Christian Athletes in Palm Beach County Florida. Along with beginning a recovery ministry at his church in South Carolina, He also has served as the National Training Coach for Celebrate Recovery in 2002, southeast regional director in 2003, and eventually became the first National Director for Celebrate Recovery in 2004.  SHOW NOTES Reach Greg via email:  ganderson@fca.org  Greg's website for Men's recovery group: mysafegroup.org   

Are you a black cat or a golden retriever? How does birth order and gender affect childhood? Benny and Lindsey dive into relationship dynamics and sibling rivalry this week, and take another shot at DeSantis (in Minecraft?) regarding a bizarre campaign ad. Benny chats with bearded drag queen and SGN writer Nemesis about drag role models, inspiration, and the satanic panic around Pokémon. RADIO SGN INFO Merch: www.redbubble.com/people/SeattleGayNews/shop Instagram: @radio.sgn Twitter: @radiosgn Music Intro: Meatball by Jesse Spillane Transitions: Night Sun by TRG Banks --- Send in a voice message: https://podcasters.spotify.com/pod/show/seattle-gay-news/message Support this podcast: https://podcasters.spotify.com/pod/show/seattle-gay-news/support

Join ALL ACCESS! Are you ready to unlock your ministry's potential? Our ALL ACCESS plan includes  courses from Steve Gladen and leading small group pastors from across the country. workshops quarterly coaching premium articles live digital events.  All for the price of $49 a month or $490 annually.You even get half off in person events like the LOBBY and ACCELERATEAnd to make it an even better deal, that one low price is a churchwide license. Everyone at your church is included. PLUS your WHOLE team gets half off the events. Meaning with this plan, your church can SAVE money and get your whole team trained and equipped.Check it out today at https://smallgroupnetwork.com/allaccess ... your community, unlocked!Guests on the Panel:Bill Search is the author of Essential Guide for Small Group Leaders and Simple Small Groups. Currently serving as Executive Pastor of Ministries at Crossings Community Church in Oklahoma City.  Heather Lee has served on staff at Crossroads Church in Manteca, California for 12 years and as the Groups Director for the last 4 years. She also serves on the SGN team as the Northern California Regional Leader.  Danny Bias is the Life Groups Champion at Newbreak Church in San Diego since 2014. Currently an blogger/author for the Small Group Network.   ALIGN + ACCELERATE July 11-13, 2023ALIGN is the small groups ministry essentials workshop. Perfect for people new to groups or who are considering launching groups at their church.ACCELERATE is a 2 day intensive workshop for a small group point person who wants to develop an 18 month strategic plan for growth or revitalization.Your team can attend ALIGN or ACCELERATE or get a discounted rate for attending both. For those attending all 3, lodging and lunches are included in the price! And don't forget, ALL ACCESS members always get 50% off all events.We'll see you there! https://smallgroupnetwork.com/conferences/

After a long hiatus, SGFY is back with a new format & new co-host! Long time friend of the show Dave Wimer now joins the SGN family and will bring a brand new perspective on music discussions, as that is what the new SGFY will focus on! This episode we spend a little time catching up and sharing stories before reviewing two new albums for 2023; Metallica's "72 Seasons" and Mudhoney's "Plastic Eternity" Listen to Alex's Metallica Mix ; "72 Seasons To Self Destruct" - https://open.spotify.com/playlist/35dZNuN8VOAYpCZN6LQgQm?si=f8f46dd3d1cf4586ARTIST: Zen ArcadeSONG: Armageddon (Brought Us Together) - The Many Faces of WeOur merch store is now LIVE! Get you a brand new t-shirt!! https://somethinggoodnetwork.bigcartel.com/Listen to other shows on the SOMETHING GOOD NETWORK: https://linktr.ee/somethinggoodnetwork Join our Discord: https://discord.gg/Ge53Nu9Click here for Dave's podcast FATTY STOKES

We have a busy Wagering Week ahead as baseball opens up for the start of the season and the Final 4 is played this weekend. SGN's Tom Barton breaks down the entire MLB season and gives his league winners and prop plays, plus everything you need to know about the Final 4. Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country? From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

After a wild first weekend in the NCAA Tournament where underdogs ruled the day, SGN's veteran wagering analyst Tom Barton examines this weekend's games. Will the dogs keep barking or will we revert back to the center? Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country? From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

Join ALL ACCESS! Are you ready to unlock your ministry's potential? Our ALL ACCESS plan includes  courses from Steve Gladen and leading small group pastors from across the country. workshops quarterly coaching premium articles live digital events.  All for the price of $49 a month or $490 annually.You even get half off in person events like the LOBBY and ACCELERATEAnd to make it an even better deal, that one low price is a churchwide license. Everyone at your church is included. PLUS your WHOLE team gets half off the events. Meaning with this plan, your church can SAVE money and get your whole team trained and equipped.Check it out today at https://smallgroupnetwork.com/allaccess ... your community, unlocked!Three Seasons of Ministry: What I Wish I Had KnownPart 3: Sustaining Steve Gladen has served as the Global Pastor of Small Groups at Saddleback Church since 1998. He oversees the strategic launch and development of over 7,000 adult small groups on multiple campuses. Steve has authored “Small Groups With Purpose,” which has translated into eight languages to date, and “Leading Small Groups With Purpose.” Steve is also the Founder of the Small Group Network (SGN) giving influence and oversight to all the SGN initiatives.In part 3 of this 3 part “Group Talk” podcast series, host Carolyn Taketa interviews Steve on the following topics in this exciting interview about 5 things to pay attention to in each season of ministry. How to build a board of directors that can help navigate & sustain life & ministry How to pour into & equip other churches & leaders Learning to trust, yet verify How to be aware of tunnel vision How to stay hungry for the long haul ALIGN + ACCELERATE July 11-13, 2023ALIGN is the small groups ministry essentials workshop. Perfect for people new to groups or who are considering launching groups at their church.ACCELERATE is a 2 day intensive workshop for a small group point person who wants to develop an 18 month strategic plan for growth or revitalization.Your team can attend ALIGN or ACCELERATE or get a discounted rate for attending both. For those attending all 3, lodging and lunches are included in the price! And don't forget, ALL ACCESS members always get 50% off all events.We'll see you there! https://smallgroupnetwork.com/conferences/

SGN's veteran wagering analyst and host Tom Barton fills out his brackets on the air and walks you through not only his process of selecting teams, but gives you notes and advice on how you can outthink your bracket mates this college basketball season! Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country? From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

Join ALL ACCESS! Are you ready to unlock your ministry's potential? Our ALL ACCESS plan includes  courses from Steve Gladen and leading small group pastors from across the country. workshops quarterly coaching premium articles live digital events.  All for the price of $49 a month or $490 annually.You even get half off in person events like the LOBBY and ACCELERATEAnd to make it an even better deal, that one low price is a churchwide license. Everyone at your church is included. PLUS your WHOLE team gets half off the events. Meaning with this plan, your church can SAVE money and get your whole team trained and equipped.Check it out today at https://smallgroupnetwork.com/allaccess ... your community, unlocked!Three Seasons of Ministry: What I Wish I Had KnownPart 2: StabilizingSteve Gladen has served as the Global Pastor of Small Groups at Saddleback Church since 1998. He oversees the strategic launch and development of over 7,000 adult small groups on multiple campuses. Steve has authored “Small Groups With Purpose,” which has been translated into eight languages to date, and “Leading Small Groups With Purpose.” Steve is also the Founder of the Small Group Network (SGN) giving influence and oversight to all the SGN initiatives.In part 2 of this 3 part “Group Talk” podcast series, host Carolyn Taketa interviews Steve on the following topics in this exciting interview about 5 things to pay attention to in each season of ministry. Learning to understand and flow with your church culture Creating a blue-print for your ministry and knowing what to focus on Stopping drift and staying true to your metrics Remembering your calling and overcoming insecurities Growing your volunteer leadership team ALIGN + ACCELERATE July 11-13, 2023ALIGN is the small groups ministry essentials workshop. Perfect for people new to groups or who are considering launching groups at their church.ACCELERATE is a 2 day intensive workshop for a small group point person who wants to develop an 18 month strategic plan for growth or revitalization.Your team can attend ALIGN or ACCELERATE or get a discounted rate for attending both. For those attending all 3, lodging and lunches are included in the price! And don't forget, ALL ACCESS members always get 50% off all events.We'll see you there! https://smallgroupnetwork.com/conferences/

The Conference Tournaments will be played this week and SGN's Tom Barton tells us how the public will overvalue good teams!  Barton also dishes on what pitfalls to be careful of when betting on afternoon games during the week. Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country? From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

Join ALL ACCESS! Are you ready to unlock your ministry's potential? Our ALL ACCESS plan includes  courses from Steve Gladen and leading small group pastors from across the country. workshops quarterly coaching premium articles live digital events.  All for the price of $49 a month or $490 annually.You even get half off in person events like the LOBBY and ACCELERATEAnd to make it an even better deal, that one low price is a churchwide license. Everyone at your church is included. PLUS your WHOLE team gets half off the events. Meaning with this plan, your church can SAVE money and get your whole team trained and equipped.Check it out today at https://smallgroupnetwork.com/allaccess ... your community, unlocked!Three Seasons of Ministry: What I Wish I Had KnownPart 1: StartingSteve Gladen has served as the Global Pastor of Small Groups at Saddleback Church since 1998. He oversees the strategic launch and development of over 7,000 adult small groups on multiple campuses. Steve has authored “Small Groups With Purpose,” which has been translated into eight languages to date, and “Leading Small Groups With Purpose.” Steve is also the Founder of the Small Group Network (SGN) giving influence and oversight to all the SGN initiatives.In part 1 of this 3 part “Here To There” podcast series, host Carolyn Taketa interviews Steve on the following topics in this exciting interview about 5 things to pay attention to in each season of ministry. The main objectives to focus on in year one of being a new small group point person. Building trust with your senior leadership (Listen to Steve discuss this more.)  Taking time to develop a blue-print for your ministry  Staring out with small steps The 5 phases of changing church culture  Building relationships & finding the right mentors to support you ALIGN + ACCELERATE July 11-13, 2023ALIGN is the small groups ministry essentials workshop. Perfect for people new to groups or who are considering launching groups at their church.ACCELERATE is a 2 day intensive workshop for a small group point person who wants to develop an 18 month strategic plan for growth or revitalization.Your team can attend ALIGN or ACCELERATE or get a discounted rate for attending both. For those attending all 3, lodging and lunches are included in the price! And don't forget, ALL ACCESS members always get 50% off all events.We'll see you there! https://smallgroupnetwork.com/conferences/

Andy Bagwell, author of "Duke Still Sucks" joins SGN's Tom Barton in this episode of Wagering Week to talk about the downfall of UNC basketball this year and the big Duke/UNC game this weekend. Plus, Tom goes in depth about the mid majors and where you can make money with them! Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country? From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

This Wagering Week, SGN's Tom Barton takes a very early look at the 2024 Super Bowl odds and gives you two teams he is already jumping in on this early. Tom also talks about the NBA All Star weekend and Tiger Woods and his immense odds! Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

The Super Bowl is this weekend and SGN's Tom Barton breaks down every angle that you need to make some money! From straight plays, to prop plays to fun exotics, Tom will give you everything you need for the big game! Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

After SGN's Tom Barton warned everyone about the overs last weekend, he breaks down this week's game and tells you who he sees going to the big game! Barton also explains why prop plays can be so vital as the playoffs move forward! Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek

It was a wild weekend last week and the sports books took some big losses! SGN's Tom Barton explains why we have to be careful this weekend and how the public can be misguided on some big NFL plays! Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

The NFL regular season is done and we are onto Wild Card Weekend! SGN's Tom Barton breaks down all of the playoff games this weekend and tells you why it's such a different animal betting the playoffs! Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

After a very emotional week in the NFL, SGN's Tom Barton takes a look at the playoff picture and explains why he thinks that Week 18 is the hardest week in the NFL to handicap! Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

The playoffs are almost here and that means many bettors will be betting on "must win" spots. SGN's Tom Barton tells you why that's a bad idea and runs through every game this week, finding the good opportunities to play!  Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

This Wagering Week brings some pretty massive QB changes around the league! Backups are the story of the week as veteran SGN wagering analyst Tom Barton breaks down all the games for you.Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

SGN's Tom Barton looks at this week's games and tells us which teams need a win to keep their playoff hopes alive. Barton also discusses how to bet “must win” scenarios.  Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

This Wagering Week, SGN's Tom Barton looks at the NFL slate of games and answers how we should react to teams that are in "must win" spots. Plus, there are some new QBs to account for and possible December weather issues to discuss! Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten #WageringWeek 

This Wagering Week, SGN's Tom Barton examines what you should consider when betting on Watson's return to Houston, plus Tom says 3 games could be the game of the week, and even a Super Bowl preview game!  Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten. #WageringWeek 

Happy Thanksgiving! We have a ton of games this weekend and as we inch closer to the playoffs SGN's Tom Barton examines the major QB changes that are impacting Week 12 in the NFL. Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten. #WageringWeek 

It's all about the weather this Wagering Week as SGN's Tom Barton talks about the effect that snow will have in Green Bay and Buffalo. Plus, how will teams bounce back after emotional wins and losses?! Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten. #WageringWeek 

This Wagering Week features the NFL trade deadline and SGN's Tom Barton talks about how those moves impacted teams moving forward.  Tom also takes a look at Week 9 in the NFL and all the huge lines on the card! Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten. #WageringWeek

This Wagering Week Tom starts with some World Series talk and gives you what he thinks are the best best for the fall classic. Then it's onto week 8 in the NFL where new QBs are starting and old QBs are struggling. Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten. #WageringWeek

Week 6 in the NFL brings some very tight lines. Every game but one is lined by oddsmakers to be a one possession game and the line makers are starting their team corrections. SGN's Tom Barton takes you through every game and tells you what to watch for this Wagering Week. Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten. #WageringWeek 

In this episode of Wagering Week, NFL injuries continue to pile up for teams all around the league.  SGN's Tom Barton factors in the "unknown" and uncovers some areas to exploit on the lines. Tom then takes listeners around the league and finds spots to make money for this weekend's games! Tune in to find out why Wagering Week is the #1 Podcast in the Sports Betting talk category! Did you know #SGN programming can be found on 100+ radio stations across the country @SportsMapRadio. From Wanna Bet?! Weekend Edition to SGN Best Bets and Wagering Week, learn why Sports Garten Network is your #1 Source for Credible Sports Wagering Intelligence! Follow us on Twitter, Facebook & Instagram @SportsGarten. #WageringWeek 

This week we're doing an ALTM Rewind with "Summer House" star, Paige DeSorbo. :) Enjoy! This week Jamie talks to Paige DeSorbo, co-star of Bravo's hit show “Summer House.” Jamie and Paige share what was just the right amount of too much this week and their quarantine beauty product favorites. They then, of course, get into all things Bravo “Summer House.” Paige talks about her fight with Lindsay and addresses whether or not she meddled with Hannah and Luke's relationship. They then talk about how awful the people on Twitter can be how this season she's received more troll comments (and how she's dealing with that drama). After that they share their thoughts on Jessica from “Love Is Blind” and Scheana from “Vanderpump Rules,” and then get straight into Paige's pantry make out sessions with Carl last summer. After that Jamie asks Paige about all things Jordan and whether or not she thinks he's in the closet (not to be confused with the pantry). Paige gives her take on the Jordan and Jules situation and what she would've done differently. Paige then answers a few questions from Jamie's followers like what surprises her about Carl and whether or not she thinks Jules should've been kicked out of the house. Paige talks about the whole “finger bang” inside joke, Amanda quitting her full time job for Lover Boy, her honest opinion on Lindsay, and the Lindsay and Carl situation. We end the episode with a “Summer House” themed game of word association and it's absolutely hilarious. Things we mention: SGN episode 2 DpHue Color Touch-Up Spray Sunday Riley CEO Glow Kiehl's Turmeric Brightening Mask Sunday Riley Good Genes Sunday Riley Ice Ceramide Moisturizing Cream Paige and Hannah's Giggly Squad PLEASE RATE, REVIEW and SUBSCRIBE! (It makes a HUGE difference- THANK YOU). Follow @ALittleTooMuchPodcast and @itsJamieStone on Instagram.

Very special guests rocking with us on this one. We have SGN and JFlow from #Foosgoneeducated , authors of the book "This Foo" a beginner guide to self improvement and personal life experiences to show for it. Do not let this go over your head , Listen and Share.  Pura Cultura , Sin Censura