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https://github.com/Snowflake-Labs/snowflake-arctic


https://github.com/Snowflake-Labs/snowflake-arctic

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[![License Apache 2.0](https://badgen.net/badge/license/apache2.0/blue)](https://github.com/Snowflake-Labs/snowflake-arctic/blob/master/LICENSE)

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# ❄️ Snowflake AI Research ❄️



## Latest News

* [07/23/2024] [Snowflake Teams Up with Meta to Host and Optimize New Flagship Model Family in Snowflake Cortex AI

](https://www.snowflake.com/blog/meta-llama-enterprise-apps-snowflake-cortex-ai/)

    * [Achieve Low-Latency and High-Throughput Inference with Meta's Llama 3.1 405B using Snowflake’s Optimized AI Stack](https://www.snowflake.com/engineering-blog/optimize-LLMs-with-llama-snowflake-ai-stack/)

    * [Fine-Tune Llama 3.1 405B on a Single Node using Snowflake’s Memory-Optimized AI Stack](https://www.snowflake.com/engineering-blog/fine-tune-llama-single-node-snowflake/)

* [04/24/2024] [Snowflake Arctic: The Best LLM for Enterprise AI — Efficiently Intelligent, Truly Open](https://www.snowflake.com/blog/arctic-open-efficient-foundation-language-models-snowflake/)

    

## Overview



The [Snowflake AI Research team](https://www.snowflake.com/en/data-cloud/workloads/ai-ml/ai-research/) is conducting open, foundational research to advance the field of AI while making enterprise AI easy, efficient, and trusted. This repo contains several artifacts to help efficiently train and inference popular LLMs in practice. We released [Arctic](https://www.snowflake.com/blog/arctic-open-efficient-foundation-language-models-snowflake/) in April of 2023 and are proud to announce the release of our Massive LLM inference and fine-tuning stacks specifically tailored to Llama 3.1 405B.



## Llama 3.1 405B



In collaboration with DeepSpeed, Hugging Face, vLLM, and the broader AI community we are excited to open-source our inference and fine-tuning stacks optimized for Llama 3.1 405B. For inference we support a massive 128K context window from day one, while enabling real-time inference with up to 3x lower end-to-end latency and 1.4x higher throughput than existing open source solutions. Please see our blog, [Achieve Low-Latency and High-Throughput Inference with Meta's Llama 3.1 405B using Snowflake’s Optimized AI Stack](https://www.snowflake.com/engineering-blog/optimize-LLMs-with-llama-snowflake-ai-stack/), that deep dive into all of these innovations. For fine-tuning we support training on a single and multi-node training environments using the latest in memory efficient training techniques such as parameter-efficient fine-tuning, FP8 quantization, ZeRO-3-inspired sharding, and targeted parameter offloading (when necessary). Please see our blog, [Fine-Tune Llama 3.1 405B on a Single Node using Snowflake’s Memory-Optimized AI Stack](https://www.snowflake.com/engineering-blog/fine-tune-llama-single-node-snowflake/), for a deep dive into how we did this.



### Getting started



* [Inference deployment and benchmarks with vLLM](inference/llama3.1)

* [Fine-Tuning Support for Llama 3.1 405B](training/llama3.1)



## Arctic



At Snowflake, we see a consistent pattern in AI needs and use cases from our enterprise customers. Enterprises want to use LLMs to build conversational SQL data copilots, code copilots and RAG chat bots. From a metrics perspective, this translates to LLMs that excel at SQL, code, complex instruction following and the ability to produce grounded answers. We capture these abilities into a single metric we call enterprise intelligence by taking an average of Coding (HumanEval+ and MBPP+), SQL Generation (Spider), and Instruction following (IFEval).










As seen here, Arctic is on par or better than both LLAMA 3 8B and LLAMA 2 70B on enterprise metrics, while using less than ½ of the training compute budget. Similarly, despite using 17x less compute budget, Arctic is on par with Llama3 70B in enterprise metrics like Coding (HumanEval+ & MBPP+), SQL (Spider) and Instruction Following (IFEval). It does so while remaining competitive on overall performance, for example, despite using 7x less compute than DBRX, it remains competitive on Language Understanding and Reasoning (a collection of 11 metrics) while being better in Math (GSM8K).










Arctic uses a unique Dense-MoE Hybrid transformer architecture. It combines a 10B dense transformer model with a residual 128x3.66B MoE MLP resulting in 480B total and 17B active parameters chosen using a top-2 gating. To learn more about this architecture please read through our blog post here.



The Snowflake AI Research Team is thrilled to introduce Snowflake Arctic, a top-tier enterprise focused LLM that pushes the frontiers of cost-effective training and openness. Arctic is efficiently intelligent and truly open.



* Efficiently Intelligent: Arctic excels at enterprise tasks such as SQL generation, coding and instruction following benchmarks even when compared to open source models trained with significantly higher compute budgets. In fact, it sets a new baseline for cost effective training to enable Snowflake customers to create high quality custom models for their enterprise needs at a low cost. 



* Truly Open: Apache 2.0 license provides ungated access to weights and code. In addition, we are also open sourcing all of our data recipes and research insights.



### Getting Started



**Inference API Providers**

Access Arctic via your model garden or catalog of choice including AWS, NVIDIA AI Catalog, Replicate, Lamini, Perplexity, and Together AI over the next coming days.



**Model Weights**

The best way to get yourself running with Arctic is through Hugging Face. We have uploaded both the Base and Instruct model variants to the Hugging Face hub:



* [Snowflake/snowflake-arctic-base](https://huggingface.co/Snowflake/snowflake-arctic-base)

* [Snowflake/snowflake-arctic-instruct](https://huggingface.co/Snowflake/snowflake-arctic-instruct)



**Inference**



We provide two different tutorials on standing up Arctic for inference:



* [Basic Hugging Face setup](inference/arctic)

* [vLLM Deployment](inference/arctic/vllm/)



**Cookbooks/Tutorials**



We believe in a thriving research community, and we are committed to sharing our insights as we build the Arctic family of models, to advance research and reduce the cost of LLM training and inference for everyone. Please check out our [on-going cookbook releases](https://www.snowflake.com/en/data-cloud/arctic/cookbook/) where we will dive deeper into several areas crucial for training models like Arctic.



* [Exploring Mixture of Experts (MoE)](https://medium.com/snowflake/snowflake-arctic-cookbook-series-exploring-mixture-of-experts-moe-c7d6b8f14d16)

* [Building an Efficient Training System for Arctic](https://medium.com/snowflake/snowflake-arctic-cookbook-series-building-an-efficient-training-system-for-arctic-6658b9bdfcae)

* [Arctic’s Approach to Data](https://medium.com/snowflake/snowflake-arctic-cookbook-series-arctics-approach-to-data-b81a8a0958bd)