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How to eliminate infrastructure friction and ship production models in hours
How to deploy and use a text-to-video model (Wan2.2-TI2V-5B) using FlexAI's inference serving capabilities
How to deploy and use a text-to-audio model (Stable Audio Open 1.0) using FlexAI's inference serving capabilities
How to deploy and use a text-to-image model (Stable Diffusion 3.5 Large) using FlexAI’s inference serving capabilities
A guide for evaluating language models using the LM-Evaluation-Harness framework
Use FlexAI to fine-tune language models using reinforcement learning (RL) techniques with EasyR1
Use FlexAI to fine-tune language models on domain-specific data using Axolotl
Demonstrates how easy it is to use FlexAI to run a training job with a couple of commands
Build a system where AI agents work together under a central supervisor
Interactive interface to ask questions based on documents using RAG
Interactive interface for recording audio messages and receiving transcriptions
Shows that only two flags are needed to start a DDP Training Job
Experiment tracking involves systematically recording and managing details of machine learning experiments, such as code, data, configurations, parameters, metrics, and results.
This blueprint will continue training from a Checkpoint emitted by the Training Job in the A Simple Training Job on FlexAI blueprint.
In some cases you might want to use large datasets that would be too large to download or push to FlexAI and you'd prefer to use that data transfer time more efficiently.
This blueprint demonstrates how to fine-tune a language model using LlamaFactory on FlexAI.
In this blueprint, we will fine-tune a causal language model using QLoRA and the SFTTrainer from trl.
The goal of this blueprint is to fine-tune the parler_tts_mini_v0.1 model to create a French version.
Fine-tune a diffusion model efficiently using LoRA
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