Summary LangChain "OpenSource LLMs" Webinar - YouTube (Youtube) www.youtube.com
10,831 words - YouTube video - View YouTube video
One Line
The LangChain webinar on YouTube discusses the significance of open source LLMs in LinkedIn, introducing Mosaic Ka's composer, streaming, and L foundry library, while emphasizing cost-effectiveness and efficiency in training methods for large models.
Slides
Slide Presentation (16 slides)
Key Points
- The LangChain "OpenSource LLMs" webinar focuses on the importance of Open Source Learning Management Systems (LLMs) in various aspects of LinkedIn.
- The webinar includes presentations from experts in the field, discussing topics such as open documentation, open code, open weights, and open data in determining whether a model is truly open source.
- Brandon, the founder and CEO of No, talks about Gp for all and how No approaches Open Source and open data.
- Daniel, a machine learning engineer at Mosaic Ka, discusses their composer, streaming, and L foundry library.
- LangChain is an open-source platform for training large language models (LLMs) that emphasizes the importance of efficient training methods and challenges of deploying large models at scale.
Summaries
87 word summary
The LangChain webinar on YouTube highlights the importance of Open Source LLMs in LinkedIn. Presentations discuss the significance of open documentation, code, weights, and data in determining if a model is truly open source. Mosaic Ka's composer, streaming, and L foundry library are introduced. The motivation behind creating Mosaic as an open-source LLM platform is explained, emphasizing cost-effectiveness and suitability for specialized AI systems. LangChain emphasizes efficient training methods and challenges in deploying large models at scale. They support startups and open-source communities in LLM training efforts.
237 word summary
The LangChain “OpenSource LLMs” webinar on YouTube includes presentations on the importance of Open Source LLMs in LinkedIn. Brandon discusses Gp for all and No's approach to Open Source and open data, while Daniel presents Mosaic Ka's composer, streaming, and L foundry library. Von talks about their OpenSource cell and training and inference stack. The presentations emphasize the significance of open documentation, code, weights, and data in determining if a model is truly open source. No's tool, Atlas, for training data analysis and improving explainability and accessibility in AI models is also discussed. The webinar concludes with upcoming releases and audience questions.
Ban and Daniel explain the motivation behind creating Mosaic as an open-source LLM platform, highlighting the cost-effectiveness of training company-specific models and the suitability of specialized AI systems for high-value workflows. They introduce Mp 7 b, the model they trained, along with Composer and streaming datasets. They invite people to check out their open-source tooling and join their community.
LangChain is an open-source platform for training large language models. The team emphasizes efficient training methods and challenges in deploying large models at scale. They discuss starting small, iterating quickly, and using their streaming feature to avoid cloud and vendor lock-in. They highlight robust evaluation methods, unit testing, open-source leaderboards, and using the Mosaic ML training stack for startups and resource-constrained communities. The team is dedicated to supporting startups and open-source communities in LLM training efforts.
326 word summary
The LangChain "OpenSource LLMs" webinar is available on YouTube and features quick introductions, 15-minute presentations, and a Q&A session. The focus is on Open Source LLMs and their importance in various aspects of LinkedIn. The first presentation by Brandon discusses Gp for all and No's approach to Open Source and open data. Daniel presents on Mosaic Ka's composer, streaming, and L foundry library, while Von talks about their OpenSource cell and training and inference stack. The presentations highlight the importance of open documentation, code, weights, and data in determining if a model is truly open source. Atlas, a tool developed by No, is also discussed for training data analysis and improving explainability and accessibility in AI models. The webinar concludes with upcoming releases and audience questions.
Ban and Daniel explain the motivation behind creating Mosaic as an open-source LLM platform. They argue for the cost-effectiveness of training company-specific models and the suitability of specialized AI systems for high-value workflows. They envision a future where people can buy an external API or build and deploy their own models. They share successful model training examples with small teams and introduce Mp 7 b, the model they trained, along with Composer and streaming datasets. They discuss architecture choices, software stack, infrastructure, data protection, privacy, inference infrastructure, hosted API, and enterprise tier. They invite people to check out their open-source tooling and join their community.
LangChain is an open-source platform for training large language models. The team emphasizes efficient training methods and challenges in deploying large models at scale. They suggest starting small, iterating quickly, and using their streaming feature to avoid cloud and vendor lock-in. They discuss robust evaluation methods, the importance of unit testing, open-source leaderboards, and using the Mosaic ML training stack for startups and resource-constrained communities. They encourage users to start with open-source tooling and scale up to the platform when needed. The team is dedicated to supporting startups and open-source communities in LLM training efforts.
697 word summary
The LangChain "OpenSource LLMs" webinar was recorded and will be accessible on YouTube. The format of the webinar includes quick introductions, 15-minute presentations from each group, and a general Q&A session. The focus of the webinar is on Open Source LLMs (Learning Management Systems) and their importance in various aspects of LinkedIn. The goal is to learn from experts in the field. The first presentation is by Brandon, the founder and CEO of No, who talks about Gp for all and how No approaches Open Source and open data. The second presentation is by Daniel, a machine learning engineer at Mosaic Ka, who discusses their composer, streaming, and L foundry library. Von, who manages the engineering team at Mosaic, also presents on their OpenSource cell and training and inference stack. The presentations highlight the importance of open documentation, open code, open weights, and open data in determining whether a model is truly open source. Brandon also discusses the use of Atlas, a tool developed by No, for training data analysis and its role in improving explainability and accessibility in AI models. He shares case studies and examples to illustrate these concepts. Brandon also emphasizes the importance of low resource models and privacy in accessible AI. The webinar concludes with a mention of upcoming releases and the opportunity for questions from the audience.
Ban and Daniel discuss the motivation behind creating Mosaic, an open-source large language model (LLM). They believe that there should be a place for both pre-trained models from big companies and models trained by individual companies. They argue that it is more cost-effective for companies to train their own models and that specialized AI systems are better suited for high-value workflows. They envision a future where people can either buy an external API or build and deploy their own models. They address the perception that building language models is difficult and expensive and provide examples of successful model training with small teams. They introduce Mp 7 b, the model they trained, and the tools they used, including Composer for training and streaming datasets for high-performance data streaming. They describe the architecture and training choices that went into creating Mp 7 b, such as using ALI for long context models and the Adam optimizer. They also discuss their software stack and infrastructure, including the Mosaic control plane and compute plane, which allows deployment on any cloud. They emphasize the importance of data protection and privacy. They mention their inference infrastructure and products, including a hosted API and an enterprise tier for customization and training. They encourage people to check out their open-source tooling and join their community. They conclude by mentioning future improvements and inviting questions from the audience.
LangChain is an open-source platform for training large language models (LLMs). The platform focuses on improving the efficiency and cost-effectiveness of LLM training. The team behind LangChain emphasizes the importance of training and inference, highlighting the need for efficient training methods and the challenges of deploying large models at scale. They suggest starting small and iterating quickly to discover the best approach. In terms of tooling, the team is proud of their streaming feature, which frees users from cloud and vendor lock-in. They also discuss the need for robust evaluation methods for LLMs, as automated metrics can have biases and human evaluation is often necessary. They encourage users to create their own test datasets and develop their own evaluation metrics. The team also mentions the importance of unit testing for LLMs, comparing it to writing unit tests for software. They believe that having a test dataset is critical to ensure that the model is performing as intended. In terms of open-source leaderboards, they suggest looking at various options but emphasize the importance of evaluating models within one's own framework. Finally, they discuss how startups and resource-constrained communities can use the Mosaic ML training stack to train their own custom models. They encourage users to start with the open-source tooling and then scale up to the platform when needed. The seamless transition allows users to easily scale their compute resources. Overall, the team at LangChain is dedicated to supporting startups and open-source communities in their LLM training efforts.
Raw indexed text (58,338 chars / 10,831 words)
Source: https://www.youtube.com/watch?v=9pmCM-JMJrE
Page title: LangChain "OpenSource LLMs" Webinar - YouTube