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Introduction
Amazon SageMaker’s latest addition, Cohere Command R fine-tuning model, enables enterprises to harness the power of large language models (LLMs) and unlock their full potential for a wide range of applications. In this post, we will explore the reasons for fine-tuning a model and the process of how to accomplish it with Cohere Command R.
AWS Announces the Availability of Cohere Command R Fine-tuning Model on Amazon SageMaker
Cohere Command R is a scalable, frontier LLM designed to handle enterprise-grade workloads with ease. Cohere Command R is optimized for conversational interaction and long context tasks. It targets the scalable category of models that balance high performance with strong accuracy, enabling companies to move beyond proof of concept and into production. The model boasts high precision on Retrieval Augmented Generation (RAG) and tool use tasks, low latency and high throughput, a long 128,000-token context length, and strong capabilities across 10 key languages.
Fine-tuning: Tailoring LLMs for Specific Use Cases
Fine-tuning is an effective technique to adapt LLMs like Cohere Command R to specific domains and tasks, leading to significant performance improvements over the base model. Evaluations of fine-tuned Cohere Command R model have demonstrated improved performance by over 20% across various enterprise use cases in industries such as financial services, technology, retail, healthcare, legal, and healthcare.
Why Fine-tuning is Important
The recommendation is to use a dataset that contains at least 100 examples. Cohere Command R uses a RAG approach, retrieving relevant context from an external knowledge base to improve outputs. However, fine-tuning allows you to specialize the model even further. Fine-tuning text generation models like Cohere Command R is crucial for achieving ultimate performance in several scenarios: domain-specific adaptation, data augmentation, and fine-grained control.
Solution Overview
In the following sections, we will walk through the steps to fine-tune the Cohere Command R model on SageMaker. This includes preparing the data, deploying a model, preparing for fine-tuning, creating an endpoint for inference, and performing inference.
Prepare the Fine-tuning Data
Before you can start a fine-tuning job, you need to upload a dataset with training and (optionally) evaluation data. First, make sure your data is in jsonl format. It should have the following structure: messages, role, and content.
Deploy a Model
Complete the following steps to deploy the model: subscribe to the Cohere Command R model on AWS Marketplace, choose View in Amazon SageMaker, and follow the instructions in the UI to create a training job.
Prepare for Fine-tuning
To fine-tune the model, you need the following: product ARN, training dataset and evaluation dataset, Amazon S3 location, and hyperparameters.
Create an Endpoint for Inference
When the fine-tuning is complete, you can create an endpoint for inference with the fine-tuned model. To create the endpoint, use the create_endpoint method. If the endpoint already exists, you can connect to it using the connect_to_endpoint method.
Perform Inference
You can now perform real-time inference using the endpoint. The following is the sample message that you use for input: message = “Classify the following text as either very negative, negative, neutral, positive or very positive: mr. deeds is, as comedy goes, very silly — and in the best way.” result = co.chat(message=message) print(result)
Clean up
After you have completed running the notebook and experimenting with the Cohere Command R fine-tuned model, it is crucial to clean up the resources you have provisioned. Failing to do so may result in unnecessary charges accruing on your account.
Summary
Cohere Command R with fine-tuning allows you to customize your models to be performant for your business, domain, and industry. Alongside the fine-tuned model, users additionally benefit from Cohere Command R’s proficiency in the most commonly used business languages (10 languages) and RAG with citations for accurate and verified information. Cohere Command R with fine-tuning achieves high levels of performance with less resource usage on targeted use cases.
Conclusion
Fine-tuning the Cohere Command R model on SageMaker enables enterprises to harness the power of large language models and unlock their full potential for a wide range of applications.
Frequently Asked Questions
Question 1: What is Cohere Command R?
Cohere Command R is a scalable, frontier LLM designed to handle enterprise-grade workloads with ease.
Question 2: What is fine-tuning?
Fine-tuning is an effective technique to adapt LLMs like Cohere Command R to specific domains and tasks, leading to significant performance improvements over the base model.
Question 3: What is RAG?
RAG (Retrieval Augmented Generation) is an approach used by Cohere Command R to retrieve relevant context from an external knowledge base to improve outputs.
Question 4: What are the benefits of fine-tuning the Cohere Command R model?
The benefits of fine-tuning the Cohere Command R model include improved performance by over 20% across various enterprise use cases, domain-specific adaptation, data augmentation, and fine-grained control.
Question 5: How do I deploy the fine-tuned model?
To deploy the fine-tuned model, you need to subscribe to the Cohere Command R model on AWS Marketplace, choose View in Amazon SageMaker, and follow the instructions in the UI to create a training job.
