Ludwig 0.8
Build and fine-tune custom LLMs on your private data
Ludwig 0.8 launched on August 10, 2023 with 109 votes, #639 of 4,347 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 3 years on.
What it does
Ludwig, open-sourced at Uber, makes it easy for any developer to build state-of-the-art ML models with its declarative interface. Ludwig v0.8 is the first low-code open-source framework optimized for efficiently building custom LLMs with your private data.
Ludwig is an open-source declarative deep learning framework. Train, fine-tune, and deploy models for text, image, tabular, audio, and LLMs using a simple YAML config file — no custom code required.
Declarative deep learning framework built for scale and efficiency. Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Tech stack: Python 3.12 | PyTorch 2.6 | Pydantic 2 | Transformers 5 | Ray 2.54 Install from PyPI. Be aware that Ludwig requires Python 3.12+. For a full tutorial, check out the official getting started guide , or take a look at end-to-end Examples . Let's fine-tune a pretrained LLM to follow instructions like a chatbot ("instruction tuning"). Create a YAML config file named model.yaml with the following: Let's build a neural network that predicts whether a given movie critic's review on Rotten Tomatoes was positive or…from ludwig.ai
Does a similar job
all alternatives →
TensorZero2025 · github.com · ▲307Hi HN! We're Gabriel & Viraj, and we're excited to open source TensorZero. To be a little cheeky, TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. 1. Integrate our model gateway 2. Send metrics or feedback 3. Unlock compounding improvements in quality, cost, and latency It enables a data & learning flywheel for LLMs by unifying: • Inference: one API for all LLMs, with <1ms P99 overhead • Observability: inference & feedback → your database • Optimization: better prompts, models, inference strategies • Experimentation:…
LLM StatsOct 2025 · llm-stats.com · ▲293I was overspending on GPT-4o. It was really hard to compare different models I could switch to, so I built this LLM comparison tool. It shows leaderboards, pricing, and performance data across 100+ LLMs (including all major providers and open-source models). Key features: - Live pricing comparisons - Benchmark Scores (MMLU, HumanEval, GPQA, etc.) - Context length vs cost analysis - Speed/throughput tests across providers - Quality vs price visualizations - Open source (all data verifiable) Try it out: https://llmstats.com I'd like to know your opinion :) Tech stack: Next.js,…
Open-Source Alternative to OpenAI Platform, for Local Models2025 · github.com · ▲93Hi everyone, we’re a small team, supported by Mozilla, who are working on re-imagining a UI for training, tuning and testing local LLMs. Everything is open source. If you’ve been training your own LLMs or have always wanted to, we’d love for you to play with the tool and give feedback on what the future development experience for LLM engineering could look like.
More ai this month
the category →
Paintings by AI models: programs run through a simulation of oil paint.
AI · 22d ago · stillwet.art
FastRouter.ai▲364Route requests to the right LLM for cost, latency & quality
AI · 23d ago · fastrouter.ai
Source: Product Hunt launch ↗
Launched alongside, August 2023
the whole month →
Resend▲1,325An entire collection of pre-built components with a much faster development experience.
Dev tools · 2023 · resend.com
Lottielab▲869Create and ship lottie animations to sites and apps faster
Dev tools · 2023 · lottielab.com
Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming. Recently we came up with a fast way to generate text that matches a regex (https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We…
AI · 2023 · github.com