Enterprises today are increasingly exploring ways to leverage large language models (LLMs) to boost productivity and create intelligent applications. However, many of the available LLM options are generic models not tailored for specialized enterprise needs like data analysis, coding, and task automation. Enter Snowflake Arctic – a state-of-the-art LLM purposefully designed and optimized for core enterprise use cases.
Developed by the AI research team at Snowflake, Arctic pushes the boundaries of what’s possible with efficient training, cost-effectiveness, and an unparalleled level of openness. This revolutionary model excels at key enterprise benchmarks while requiring far less computing power compared to existing LLMs. Let’s dive into what makes Arctic a game-changer for enterprise AI.
Enterprise Intelligence Redefined At its core, Arctic is laser-focused on delivering exceptional performance on metrics that truly matter for enterprises – coding, SQL querying, complex instruction following, and producing grounded, fact-based outputs. Snowflake has combined these critical capabilities into a novel “enterprise intelligence” metric.
The results speak for themselves. Arctic meets or outperforms models like LLAMA 7B and LLAMA 70B on enterprise intelligence benchmarks while using less than half the computing budget for training. Remarkably, despite utilizing 17 times fewer compute resources than LLAMA 70B, Arctic achieves parity on specialized tests like coding (HumanEval+, MBPP+), SQL generation (Spider), and instruction following (IFEval).
But Arctic’s prowess goes beyond just acing enterprise benchmarks. It maintains strong performance across general language understanding, reasoning, and mathematical aptitude compared to models trained with exponentially higher compute budgets like DBRX. This holistic capability makes Arctic an unbeatable choice for tackling the diverse AI needs of an enterprise.
The Innovation
Dense-MoE Hybrid Transformer So how did the Snowflake team build such an incredibly capable yet efficient LLM? The answer lies in Arctic’s cutting-edge Dense Mixture-of-Experts (MoE) Hybrid Transformer architecture.
Traditional dense transformer models become increasingly costly to train as their size grows, with computational requirements increasing linearly. The MoE design helps circumvent this by utilizing multiple parallel feed-forward networks (experts) and only activating a subset for each input token.
However, simply using an MoE architecture isn’t enough – Arctic combines the strengths of both dense and MoE components ingeniously. It pairs a 10 billion parameter dense transformer encoder with a 128 expert residual MoE multi-layer perceptron (MLP) layer. This dense-MoE hybrid model totals 480 billion parameters but only 17 billion are active at any given time using top-2 gating.
The implications are profound – Arctic achieves unprecedented model quality and capacity while remaining remarkably compute-efficient during training and inference. For example, Arctic has 50% fewer active parameters than models like DBRX during inference.
But model architecture is only one part of the story. Arctic’s excellence is the culmination of several pioneering techniques and insights developed by the Snowflake research team:
- Enterprise-Focused Training Data Curriculum Through extensive experimentation, the team discovered that generic skills like commonsense reasoning should be learned early, while more complex specializations like coding and SQL are best acquired later in the training process. Arctic’s data curriculum follows a three-stage approach mimicking human learning progressions.
The first teratokens focus on building a broad general base. The next 1.5 teratokens concentrate on developing enterprise skills through data tailored for SQL, coding tasks, and more. The final teratokens further refine Arctic’s specializations using refined datasets.
- Optimal Architectural Choices While MoEs promise better quality per compute, choosing the right configurations is crucial yet poorly understood. Through detailed research, Snowflake landed on an architecture employing 128 experts with top-2 gating every layer after evaluating quality-efficiency tradeoffs.
Increasing the number of experts provides more combinations, enhancing model capacity. However, this also raises communication costs, so Snowflake landed on 128 carefully designed “condensed” experts activated via top-2 gating as the optimal balance.
- System Co-Design But even an optimal model architecture can be undermined by system bottlenecks. So the Snowflake team innovated here too – co-designing the model architecture hand-in-hand with the underlying training and inference systems.
For efficient training, the dense and MoE components were structured to enable overlapping communication and computation, hiding substantial communication overheads. On the inference side, the team leveraged NVIDIA’s innovations to enable highly efficient deployment despite Arctic’s scale.
Techniques like FP8 quantization allow fitting the full model on a single GPU node for interactive inference. Larger batches engage Arctic’s parallelism capabilities across multiple nodes while remaining impressively compute-efficient thanks to its compact 17B active parameters.
With an Apache 2.0 license, Arctic’s weights and code are available ungated for any personal, research or commercial use. But Snowflake has gone much farther, open-sourcing their complete data recipes, model implementations, tips, and the deep research insights powering Arctic.
The “Arctic Cookbook” is a comprehensive knowledge base covering every aspect of building and optimizing a large-scale MoE model like Arctic. It distills key learnings across data sourcing, model architecture design, system co-design, optimized training/inference schemes and more.
From identifying optimal data curriculums to architecting MoEs while co-optimizing compilers, schedulers and hardware – this extensive body of knowledge democratizes skills previously confined to elite AI labs. The Arctic Cookbook accelerates learning curves and empowers businesses, researchers and developers globally to create their own cost-effective, tailored LLMs for virtually any use case.
Getting Started with Arctic
For companies keen on leveraging Arctic, Snowflake offers multiple paths to get started quickly:
Serverless Inference: Snowflake customers can access the Arctic model for free on Snowflake Cortex, the company’s fully-managed AI platform. Beyond that, Arctic is available across all major model catalogs like AWS, Microsoft Azure, NVIDIA, and more.
Start from Scratch: The open source model weights and implementations allow developers to directly integrate Arctic into their apps and services. The Arctic repo provides code samples, deployment tutorials, fine-tuning recipes, and more.
Build Custom Models: Thanks to the Arctic Cookbook’s exhaustive guides, developers can build their own custom MoE models from scratch optimized for any specialized use case using learnings from Arctic’s development.
A New Era of Open Enterprise AI Arctic is more than just another powerful language model – it heralds a new era of open, cost-efficient and specialized AI capabilities purpose-built for the enterprise.
From revolutionizing data analytics and coding productivity to powering task automation and smarter applications, Arctic’s enterprise-first DNA makes it an unbeatable choice over generic LLMs. And by open sourcing not just the model but the entire R&D process behind it, Snowflake is fostering a culture of collaboration that will elevate the entire AI ecosystem.
As enterprises increasingly embrace generative AI, Arctic offers a bold blueprint for developing models objectively superior for production workloads and enterprise environments. Its confluence of cutting-edge research, unmatched efficiency and a steadfast open ethos sets a new benchmark in democratizing AI’s transformative potential.
Here’s a section with code examples on how to use the Snowflake Arctic model:
Hands-On with Arctic
Now that we’ve covered what makes Arctic truly groundbreaking, let’s dive into how developers and data scientists can start putting this powerhouse model to work.
Out of the box, Arctic is available pre-trained and ready to deploy through major model hubs like Hugging Face and partner AI platforms. But its real power emerges when customizing and fine-tuning it for your specific use cases.
Arctic’s Apache 2.0 license provides full freedom to integrate it into your apps, services or custom AI workflows. Let’s walk through some code examples using the transformers library to get you started:
Basic Inference with Arctic
For quick text generation use cases, we can load Arctic and run basic inference very easily:
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Snowflake/snowflake-arctic-instruct")
model = AutoModelForCausalLM.from_pretrained("Snowflake/snowflake-arctic-instruct")
# Create a simple input and generate text
input_text = "Here is a basic question: What is the capital of France?"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
# Generate response with Arctic
output = model.generate(input_ids, max_length=150, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
This should output something like:
“The capital of France is Paris. Paris is the largest city in France and the country’s economic, political and cultural center. It is home to famous landmarks like the Eiffel Tower, the Louvre museum, and Notre-Dame Cathedral.”
As you can see, Arctic seamlessly understands the query and provides a detailed, grounded response leveraging its robust language understanding capabilities.
Fine-tuning for Specialized Tasks
While impressive out-of-the-box, Arctic truly shines when customized and fine-tuned on your proprietary data for specialized tasks. Snowflake has provided extensive recipes covering:
- Curating high-quality training data tailored for your use case
- Implementing customized multi-stage training curriculums
- Leveraging efficient LoRA, P-Tuning orFactorizedFusion fine-tuning approaches
- Optimizations for discerning SQL, coding or other key enterprise skills
Here’s an example of how to fine-tune Arctic on your own coding datasets using LoRA and Snowflake’s recipes:
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training
# Load base Arctic model
tokenizer = AutoTokenizer.from_pretrained("Snowflake/snowflake-arctic-instruct")
model = AutoModelForCausalLM.from_pretrained("Snowflake/snowflake-arctic-instruct", load_in_8bit=True)
# Initialize LoRA configs
lora_config = LoraConfig(
r=8,
lora_alpha=16,
target_modules=["query_key_value"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
# Prepare model for LoRA finetuning
model = prepare_model_for_int8_training(model)
model = get_peft_model(model, lora_config)
# Your coding datasets
data = load_coding_datasets()
# Fine-tune with Snowflake's recipes
train(model, data, ...)
This code illustrates how you can effortlessly load Arctic, initialize a LoRA configuration tailored for code generation, and then fine-tune the model on your proprietary coding datasets leveraging Snowflake’s guidance.
Customized and fine-tuned, Arctic becomes a private powerhouse tuned to deliver unmatched performance on your core enterprise workflows and stakeholder needs.