#28: Open Source LLMs at scale & LLMs Ebook release ??
?? Deploying Open Source LLMs at scale
Deploying open-source Large Language Models (LLMs) at scale while ensuring reliability, low latency, and cost-effectiveness can be a challenging endeavor.
Drawing from our extensive experience in constructing LLM infrastructure and successfully deploying it for our clients, we have compiled a list of the primary challenges commonly encountered by individuals in this process.
Read our last few blogs on LLM & Gen AI Series: ????
Use of ML in conversational assistants ??
In our latest episode of True ML Talks, Denys Linkov, Head of ML at Voiceflow speaks about the following aspects:?
? Discussion on the impact of recent GPT models on the ML landscape
? Obstacles encountered by Voiceflow when shifting from NLP-based solutions to alternative models
? Denys’ journey as one of the earliest members in establishing Voiceflow's ML infrastructure
? Solutions employed by Voiceflow to implement autoscaling
? Voiceflow’s choice of specific model server after testing various latency-sensitive models
? Comprehensive details regarding the development of RAG systems at Voiceflow
MLOps.Community Slack Discussions ??
Below are a summary of some of the informative conversations on the most popular MLOps community:
? ?? Terraform or Cloud Formation
Summary: Terraform is favored over Cloud Formation for AWS infrastructure due to its cloud-agnostic nature and transferable skills. However, it's noted that a single Terraform file can't deploy to multiple clouds. Cloud Formation is criticized for being cumbersome and requiring manual fixes. Alternatives like AWS CDK and Pulumi are recommended for greater flexibility. Best practices suggest avoiding monolithic Terraform files and separating projects into different repositories.?
?? LLMs to assess the quality of text
Summary: ?A key concern is the reliability in using LLMs to automate the quality assessment of text in sales emails is it's challenging to validate the model's performance. An initial step could be to use human-generated content to create a validation set, then assess LLM-generated content. Existing tools like Hemingway App and Grammarly are useful for style and tone but fall short in evaluating content relevance to user queries. As it stands, the complete automation using LLMs is complex and may not guarantee high-quality responses. Human oversight is often necessary, and LLMs may be more beneficial at a larger scale, such as handling a high volume of emails..
??? Open source llms to generate synthetic QA datasets
领英推荐
Summary: LLMDataHub is a GitHub repository offering high-quality training data for Language Models, including QA datasets. "Textbooks Are All You Need" is a research paper that outlines a technique for creating synthetic QA datasets using textbooks. SELF-INSTRUCT is another research paper that describes a method for generating synthetic QA datasets specifically using Large Language Models (LLMs).
E-book: Unlocking the Power of LLMs ??
?? Early Access to Subscribers ??
We’ve co-authored Everything Large Language models with LinkedIn Top Voice Greg Coquillo, Product Manager at Amazon, to give you a comprehensive guide that delves into the fascinating world of Large Language Models (LLMs) and their applications! Dive into specific use cases of LLMs in Finance, Healthcare, E-commerce and Retail, Media and Publishing, and Customer Service and Support. Discover how LLMs are revolutionising these sectors and unlocking new possibilities.
Whether you're a business leader, data scientist, or simply curious about the power of language models, this eBook is a valuable resource for your knowledge arsenal.
?Below are the key takeaways:
?? Understand the fundamentals and significance of Large Language Models.
?? Explore the training and fine-tuning processes behind these models.
?? Discover practical applications in content creation, support systems, and more.
?? Examine real-world case studies illustrating the impact of Large Language Models.
?? Gain insights into their role in transforming finance, healthcare, and e-commerce industries.
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Brief about TrueFoundry!
Just as a reminder, for the new members of our community, TrueFoundry is a comprehensive ML/LLM Deployment PaaS, empowering ML Teams in enterprises to test, and deploy ML/LLMs?with ease while ensuring the following benefits:
??Full security for the infra team.
??40% lower costs due to Resource Management.
??90% faster deployments with SRE best practices.
For LLM/GPT style model deployment, we allow users to select pre-configured models from our catalog and fine-tune them on their datasets.
Sent with ?? by TrueFoundry