Autonomous Fine-tuning
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Autonomous Fine-tuning is a powerful feature offered by Portkey AI that enables organizations to automatically create, manage, and execute fine-tuning jobs for Large Language Models (LLMs) across multiple providers.
This feature leverages your existing API usage data to continuously improve and customize LLM performance for your specific use cases.
Automated Workflow: Streamline the entire fine-tuning process from data preparation to model deployment.
Multi-Provider Support: Fine-tune models across 10+ providers, including OpenAI, Azure, AWS Bedrock, and Anyscale.
Data-Driven Improvements: Utilize your actual API usage data to create relevant and effective fine-tuning datasets.
Continuous Learning: Set up periodic fine-tuning jobs to keep your models up-to-date with the latest data.
Enhanced Performance: Improve model accuracy and relevance for your specific use cases.
Cost-Effective: Optimize your LLM usage by fine-tuning models to better suit your needs, potentially reducing the number of API calls required.
Centralized Management: Manage all your fine-tuning jobs across different providers from a single interface.
Log Collection: Portkey's AI gateway automatically collects and stores logs from your LLM API requests.
Data Enrichment:
Filter logs based on various criteria.
Annotate logs with additional information.
Use Portkey's Guardrails feature for automatic log annotation.
Dataset Creation: Utilize filters to select the most relevant logs for your fine-tuning dataset.
Data Export: Export the enriched logs as a dataset suitable for fine-tuning.
Model Selection: Choose from a wide range of supported LLM providers and models.
Job Configuration: Set up fine-tuning parameters through an intuitive UI.
Execution: Portkey triggers the fine-tuning job on the selected provider's platform.
Monitoring: Track the progress of your fine-tuning jobs through Portkey's dashboard.
Deployment: Once complete, the fine-tuned model becomes available for use through Portkey's API gateway.
Data Collection: As you use Portkey's AI gateway for LLM requests, logs are automatically collected and stored in your Portkey account.
Data Enrichment:
Apply filters to your log data.
Add annotations and additional context to logs.
Utilize Portkey's Guardrails feature for automatic input/output annotations.
Dataset Creation:
Use the enriched log data to create a curated dataset for fine-tuning.
Apply additional filters to select the most relevant data points.
Fine-tuning Job Setup:
Access the Fine-tuning feature in Portkey's UI.
Select your desired LLM provider and model.
Choose your prepared dataset.
Configure fine-tuning parameters.
Job Execution:
Portkey initiates the fine-tuning job on the chosen provider's platform.
Monitor the progress through Portkey's dashboard.
Model Deployment:
Once fine-tuning is complete, the new model becomes available through Portkey's API gateway.
Continuous Improvement (Optional):
Set up periodic fine-tuning jobs (daily, weekly, or monthly).
Portkey automatically creates and executes these jobs using the latest data.
Portkey AI has established partnerships to extend the capabilities of its Autonomous Fine-tuning feature:
OpenPipe: Integration allows Portkey's enriched data to be used on OpenPipe's fine-tuning platform.
Pipeshift: Portkey's datasets can be seamlessly utilized in Pipeshift's inference platform.
These partnerships enable users to leverage Portkey's data preparation capabilities with specialized fine-tuning and inference services.
To begin using Autonomous Fine-tuning:
Ensure you have an active Portkey AI account with the AI gateway set up.
Navigate to the Fine-tuning section in your Portkey dashboard.
Follow the step-by-step wizard to create your first fine-tuning job.
For assistance, consult our detailed documentation or contact Portkey support.
Regularly review and update your data filtering criteria to ensure the quality of your fine-tuning datasets.
Start with smaller, focused datasets before scaling up to larger fine-tuning jobs.
Monitor the performance of your fine-tuned models and iterate as needed.
Leverage Portkey's analytics to gain insights into your model's performance improvements.
By utilizing Portkey AI's Autonomous Fine-tuning feature, you can harness the power of your own data to create customized, high-performing LLMs tailored to your specific needs, all while streamlining the management of multiple AI providers.