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Train Across Providers: Avoid getting locked into a single API or model. Open up multiple LLM providers (OpenAI, AI21, Replicate, Anthropic, Groq, Gemini) through a unified interface.
Work Together: Invite the team to keep track of training data and fine-tuning jobs in one place. Count tokens, estimate costs, evaluate performance, and compare hyperparameters to see what works best.
Write Templates: The prompt and structure of fine-tuning data have a big impact on outcomes. The templating engine lets users iterate rapidly to see what structure, labels, and prompts yield the best results.
Import & Export: Getting data in and out is easy. Export the entire dataset as a JSONL anytime in the syntax and structure of choice.
Share Models: Deploy a frontend to the fine-tuned model with a single click and share it for testing. All completions are saved to catch problems and augment the dataset.
Avoid Common Pitfalls: Fine-tuning has a reputation for being finicky. The platform deals with all the nuances for different models, from syntax to token limits, to get the desired results the first time.
No Code Required: All APIs from top LLM providers are implemented with a user-interface to make them more accessible, with full access to underlying hyperparameters and key settings without coding.

Higher Quality: Fine-tuning acts as an upgrade to few-shot learning that bakes the examples into the model itself to get better quality from prompts.
Faster Generation: For simpler tasks, train a lighter model to perform at or above the level of a higher quality model, greatly reducing latency and cost.
More Predictable Outputs: Train the model not to respond in certain ways to users, for safety, to protect the brand, and to get the formatting right.
Scales With Your Team: Cover edge cases and steer model behavior by adding examples to the dataset, instead of running into conflicts from trying to make changes to a single epic prompt.

Content: Produce high-quality reports, blog articles, social media posts, emails, and more.
Tagging & Classification: Segment data and tag content for search, metadata, or features.
Data Extraction: Extract key values from unstructured data in a consistent format.
Prioritization: Prioritize support issues, bug reports, lead form submissions and more.
Recommendations: Suggest products that a user might want based on items in their shopping cart or order history.
Fraud Detection: Train a model to determine if activity is suspicious or high-risk.
Moderation: Detect and flag inappropriate content in inboxes, apps, and chats.
Data Enrichment: Populate new fields for data, like industry or custom segments for business contacts.
Scoring & Ranking: In a RAG workflow, use a fine-tuned LLM to rerank a set of results by relevance.