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Snorkel AI

Building AI-Native Systems for Federal Infrastructure: A Conversation with Rezaur Rahman Code World Models and AutoHarness for LLM Agents Benchtalks #1: Alex Shaw (Terminal-Bench, Harbor) – Building the Benchmark Factory Building FinQA: An Open RL Environment for Financial Reasoning Agents How Tool Discipline Let a 4B Model Outsmart a 235B Giant on Financial Tasks Coding agents don’t need to be perfect, they need to recover Closing the Evaluation Gap in Agentic AI SlopCodeBench: Measuring Code Erosion as Agents Iterate Introducing the Snorkel Agentic Coding Benchmark 2026: The year of environments Part V: Future Direction and Emerging Trends in Rubric-Based AI Evaluation The self-critique paradox: Why AI verification fails where it’s needed most Chat With the Terminal-Bench Team | Snorkel AI Intelligence per watt: A new metric for AI’s future Terminal-Bench 2.0: Raising the bar for AI agent evaluation Snorkeling in RL environments Introducing SnorkelSpatial: A Benchmark for LLM Spatial Reasoning Scaling Trust: Rubrics in Snorkel's Quality Process Evaluating Multi-Agent Systems in Enterprise Tool Use Evaluating Coding Agents with Terminal-Bench 2.0 Parsing isn’t neutral: why evaluation choices matter The science of rubric design The right tool for the job: An A-Z of rubrics Data quality and rubrics: how to build trust in your models Building the benchmark: inside our agentic insurance underwriting dataset Evaluating AI agents for insurance underwriting LLM observability: key practices, tools, and challenges Anthropic Claude + AWS: revolutionizing pharma data analytics with Snorkel AI Data-centric development of an enterprise AI agent with Snorkel Building the data development platform for specialized AI LLM-as-a-judge for enterprises: evaluate model alignment at scale Why GenAI evaluation requires SME-in-the-loop for validation and trust Research spotlight: is long chain-of-thought structure all that matters when it comes to LLM reasoning distillation? Why enterprise GenAI evaluation requires fine-grained metrics to be insightful What is specialized GenAI evaluation, and why is it so critical to enterprise AI? LLM alignment techniques: 4 post-training approaches Research spotlight: Is intent analysis the key to unlocking more accurate LLM question answering? Why enterprises should embrace LLM distillation Retrieval-augmented generation (RAG) failure modes and how to fix them What is large language model (LLM) alignment? Databricks + Snorkel Flow: integrated, streamlined AI development How LLM evaluation drives better models in Snorkel Flow Unlock proprietary data with Snorkel Flow and Amazon SageMaker LLM evaluation in enterprise applications: a new era in ML Snorkel AI joins the AWS ISV Accelerate Program and launches Snorkel Flow Availability in AWS Marketplace AI data development: a guide for data science projects SnorkelCon 2024: Inaugural Snorkel AI user conference gathers leaders from 30+ Fortune 500 companies Snorkel Flow 2024.R3: Supercharge your AI development with enhanced data-centric workflows Explore the new GenAI Evaluation Suite: Snorkel 2024.R3 New NLP features in Snorkel Flow 2024.R3 Enterprise data compliance and security review: Snorkel Flow 2024.R3 How a global financial services company built a specialized AI copilot accurate enough for production Task Me Anything: innovating multimodal model benchmarks Alfred: Data labeling with foundation models and weak supervision RAG: LLM performance boost with retrieval-augmented generation Call center AI for customer experience management: a case study New GenAI features, data annotation: Snorkel Flow 2024.R2 How data slices transform enterprise LLM evaluation Meta’s Llama 3.1 405B is the new Mr. Miyagi, now what? 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Meta’s new Llama 3.1 models are here! Are you ready for it?
kedia · 2024-07-23 · via Snorkel AI

Meta released Llama 3.1 today signaling a new era of open source AI. The Llama 3.1 models include 8b, 70B, LlamaGuard, and the new Llama 3.1 405B, the largest open source foundation model with over 400 billion parameters.  Llama 3.1 performance will rival OpenAI’s GPT-4 model, despite using less than half the parameters.

This is an important milestone for the AI development stack. For AI to flourish in the enterprise, businesses need to have choices. Enterprises want to adopt foundation models that best fit their use case and then specialize those models to achieve production quality performance against domain and business-specific criteria.

With Meta’s release of open source foundation models that match the performance of proprietary models at a lower cost, the market benefits from more options to build custom AI applications.

Day 1 availability: Llama 3.1 models available in Snorkel today

As of today, all Meta Llama 3.1 large language models are natively available as part of the Prompt Builder in Snorkel Flow:

Here’s a quick demo of how easy it is to activate Meta Llama 405B in Snorkel Flow. AI development teams can access Meta’s industry-leading Llama 3.1 models from their service of choice using Hugging Face, Together AI, Microsoft Azure ML, AWS SageMaker, and Google Vertex AI Model Garden.

The growing importance of OSS in the enterprise

With the launch of Llama 3.1 Meta has not been shy about its stance that AI will be driven by open source.  Mark Zuckerberg authored a blog today, Open Source AI Is the Path Forward, that outlines the parallels between early proprietary AI model development and early proprietary OS software development. He forecasts that closed, proprietary AI models will be replaced by open source models just as Unix was replaced by Linux as the dominant OS.

The reason for that is that, especially when it comes to infrastructure, developers and enterprises benefit from having more adaptable, affordable, and better foundations from which to build. Beyond the OS we have seen this play out in databases with the rise of MySQL and later MongoDB, as well as with key technologies that accelerated cloud adoption like Kafka and K8s.

We agree that open source models are likely to gain widespread enterprise adoption. This provides enterprises more flexibility in model size, data controls, and location, as well as affordability. The Llama 3.1 release shows that very soon open source models will also provide best-in-class performance.

Paired with Snorkel Flow’s data development capabilities, the LLama 3.1 model family will help enterprises cross the chasm from prototype to production:

  • LLM specialization via fine-tuning/alignment – With Snorkel Flow, enterprises can leverage a powerful combination of Llama 3.1 outputs with their own domain expertise to create fine-tuning datasets more quickly than ever.
  • SLM for real, practical use cases – The Wall Street Journal recently covered this trend, noting: “This category of AI software—called small or medium language models—is trained on less data and often designed for specific tasks.” Llama 3.1 can now be used to build SLMs via synthetic data & distillation approaches in Snorkel Flow.
  • LLM evals – Snorkel allows teams to combine powerful evaluators like Llama Guard 3 safety model with your own guardrails & acceptance criteria.

Meta’s continued leadership in open source model development is a critical step towards more efficient, lower cost AI systems that incorporate a variety of models and route queries based on best fit. The pattern here is not far off from the use of micro-service where orchestration enables a far more efficient use of compute resources with better outcomes. Fortunately, most of the major inference providers have also announced that they are supporting Llama 3.1 which means most enterprises are in a good position to quickly adopt the Llama models.

We believe Meta’s release today was a push towards flexibility—flexibility to choose the best foundation model for your task and specialize from there. The Llama 3.1 models provide another great option for our customers as they look for the right foundation model. We encourage enterprises to seriously consider the new Meta offerings.

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