oumi

Free

Easily fine-tune, evaluate and deploy gpt-oss, Qwen3, DeepSeek-R1, or any open source LLM / VLM!

Model APIsFreeFree tier

Inputs: text

Type

Open Source

Visit Website GitHub (9.2k stars)

About oumi

Oumi is an AI-native platform that automates the entire custom model development lifecycle. It enables users to build and deploy specialized models from a plain-English description in as little as two hours. The platform covers evaluation (automatically generating failure mode analysis), data synthesis (creating targeted training examples), training (using SFT, PEFT, LoRA, QLoRA, and on-policy distillation to outperform frontier models), and deployment (with full ownership of model weights and no vendor lock-in). It supports fine-tuning open-source LLMs/VLMs like gpt-oss, Qwen3, DeepSeek-R1, and others, and claims up to 50% higher accuracy and 90% lower cost compared to generic frontier models. Oumi offers a free tier with credits and paid Pro/Enterprise plans for scaling.

Key Features

Automatically generate evaluations tailored to your use case with failure mode analysis

Synthesize targeted training examples from failure modes

Train custom models to outperform frontier models in hours using SFT, PEFT (LoRA, QLoRA), and on-policy distillation

Deploy models with full ownership, no vendor lock-in, and no deprecation timelines

Data analysis and curation capabilities

Open/closed model evaluation with failure modes

Multiple concurrent jobs for higher efficiency (Pro tier)

Production inference with autoscaling (Pro tier)

BYOC - VPC on-prem and dedicated capacity up to 1000s of GPUs (Enterprise)

Advanced training methods including RL and custom pipelines (Enterprise)

Pros & Cons

Pros

Up to 50% higher accuracy on specific tasks compared to frontier models
Up to 90% lower cost than using generic frontier models
As low as 2 hours from prompt to production deployment
Full ownership of model weights and data, no vendor lock-in
Automates evaluation, data synthesis, training, and deployment in one platform
New model releases improve existing custom models without starting over
Smaller custom models offer low latency for real-time applications
Free tier available with credits to get started

Cons

Production inference pricing can be high (e.g., $10/GPU-hr for B200-180GB)
Free tier has limited credits ($50 corporate email, $25 personal) and time-bound (3 months)
Requires clear task description and failure mode definition for optimal results
Platform dependency for the automation loop, though models can be deployed elsewhere

Best For

Building custom models for specific tasks where frontier models are too expensive or inaccurateReplacing rented generic models with owned, task-specific models to reduce cost and improve accuracyOptimizing agentic workflows that require low latency across many model callsRapid prototyping and production deployment of fine-tuned open-source LLMs/VLMs

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FAQ

Is there a free trial available?