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Layers / MLOps / Lifecycle / Training and fine-tuning

Training and fine-tuning

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Training and fine-tuning runs: distributed training, LoRA and adapters, the harness around a job. Deciding which run was better is experiment tracking.

25 in this category
SORTED BY STARS · 3 UNMEASURED, GROUPED LAST

NARROW BY WHAT THIS CATEGORY ASKS

LICENCE · 5 NOT READ

OPEN SOURCE · 12 NOT READ

HARDWARE · 15 NOT READ

PRODUCTUSED BYSTARSDOWNLOADS 30D
unslothLocal UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4, MiniMax-H3, Gemma 4, FLUX and more.76,539made-with-mlLearn how to develop, deploy and iterate on production-grade ML applications.49,459rayRay is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.43,883Megatron CoreOngoing research training transformer models at scale17,968LudwigLow-code framework for building custom LLMs, neural networks, and other AI models11,755Weights & BiasesA CLI and library for interacting with the Weights & Biases API.2311,247OumiEasily fine-tune, evaluate and deploy Qwen, Gemma, or any open weight LLM!9,384SoupFine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.6,160higgsfieldFault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters5,790h2o-llmstudioH2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://docs.h2o.ai/h2o-llmstudio/5,180fedmlFEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on a4,065Determined AIDetermined AI: The fastest and easiest way to build deep learning models.3,23963,392OonetrainerOneTrainer is a one-stop solution for all your Diffusion training needs.3,203Roboflow Maestrostreamline the fine-tuning process for multimodal models: PaliGemma 2, Florence-2, and Qwen2.5-VL2,697xTuringBuild, personalize and control your own LLMs. From data pre-processing to fine-tuning, xTuring provides an easy way to personalize open-source LLMs. Join our discord community: https://discord.gg/TgHX2,674ortFast ML inference & training for ONNX models in Rust2,506Aai-engineering.academyMastering Applied AI, One Concept at a Time2,380dstackVendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.2,244custom-diffusionCustom Diffusion: Multi-Concept Customization of Text-to-Image Diffusion (CVPR 2023)1,976openmldbOpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.1,711Neptune.ai📘 The experiment tracker for foundation model training1623125,702fasteraiFasterAI: Prune and Distill your models with FastAI and PyTorch267Amazon SageMakerOpen source library for training and deploying models on Amazon SageMaker.217,754,171IBM watsonx.aiIBM's enterprise studio for tuning and serving foundation models, including the Granite family.TectonTecton Python SDK

SORTED BY ONE STATED COLUMN · USED BY = LISTED PRODUCTS DEPENDING ON IT, IN THIS DIRECTORY — NOT A POPULARITY FIGURE · NO OVERALL SCORE AND NO RANK COLUMN, BECAUSE BLENDING ADOPTION, STARS AND DOWNLOADS NEEDS WEIGHTS, AND WEIGHTS ARE AN OPINION PRESENTED AS ARITHMETIC

Cite this page

The count and its date sit inside the citation, because “Training and fine-tuning” without a date is a claim that quietly goes wrong as the category fills.

infratoapp, “Training and fine-tuning — MLOps / Lifecycle”, read 27 October 2026. 25 listed products. https://www.infratoapp.com/layers/mlops/training

No overall score, and no rank column

Blending adoption, stars and downloads needs weights, and weights are an opinion presented as arithmetic — so this table sorts by one stated column at a time and shows which. This view holds no billing column and joins no billing table: a sponsor cannot appear higher, and the signals meter is a row’s own confidence, never a score you can sort on.

Frequently asked

What is in the Training and fine-tuning category?
25 products in the MLOps / Lifecycle layer of the AI stack. Training and fine-tuning runs: distributed training, LoRA and adapters, the harness around a job. Deciding which run was better is experiment tracking. Listed members include unsloth, made-with-ml and ray. Every fact on each listing carries the page it was read from and the date it was read.
How are Training and fine-tuning compared here?
By the 7 attributes this category declares, asked of every member and answered only where a source states it. A product with no answer on record shows a gap rather than a guess, and no blended score is published — weights would be an opinion presented as arithmetic.
unsloth or made-with-ml?
Both are listed under Training and fine-tuning. We publish no verdict between them — we hold no benchmark that would support one. What we do hold is the same attribute sheet for each, side by side with the source behind every value, at /compare/made-with-ml-vs-unsloth.
What does a blank mean on a training and fine-tuning row?
That nobody has told us, not that the answer is no. A hollow ring is "no answer on record", which is a different state from a recorded no — and the two look different on purpose. A listing with a blank has not failed the question; it has not been read for it.
How often are training and fine-tuning figures re-read?
Every value carries the date it was read and a link to the page it came from. Prices move fastest and are re-read most often; an attribute read weeks ago says so rather than presenting itself as current. Anything past 21 days is hidden rather than shown stale.
Is this every training and fine-tuning there is?
No. This is 25 we have read and can cite. A directory that claimed completeness would be making a promise it cannot keep, so the page states its own count instead and says when a category is too thin to shortlist from.
Can a vendor pay to rank higher here?
No. Nothing on this page can be bought: a sponsor cannot enter a table, reorder a row or change a mark, and there is no ranked position for sale. How the site is paid for, and the line each source of money does not cross, is set out under money and independence at /our-data#sec5.