FinetuneDB – AI fine-tuning platform to create custom LLMs
Hey HN! We’re building FinetuneDB (https://finetunedb.com/), an LLM fine-tuning platform. It enables teams to easily create and manage high-quality datasets, and streamlines the entire workflow from fine-tuning to serving and evaluating models with domain experts. You can check out our docs here: (https://docs.finetunedb.com/) FinetuneDB exists because creating and managing high-quality datasets is a real bottleneck when fine-tuning LLMs. The quality of your data directly impacts the performance of your fine-tuned models, and existing tools didn’t offer an easy…
FinetuneDB – AI fine-tuning platform to create custom LLMs launched on October 9, 2024 with 152 votes, #308 of 5,185 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
FinetuneDB is an LLM fine-tuning platform that helps teams create and manage high-quality datasets for training custom language models. It streamlines the entire workflow from dataset creation and fine-tuning to model serving and evaluation with domain experts. The platform addresses the bottleneck of dataset quality and management, which directly impacts fine-tuned model performance. It is built for AI startups and businesses looking to develop models tailored to their specific needs and domains.
written from the facts on this page · September 2026
From the sources
Easily create and manage datasets to fine-tune LLMs for cheaper, faster, and better performance.
Train AI models with your data in minutes, not weeks, and get better performance at lower cost. Capture production data and send it fully async to FinetuneDB. You have full control on what is sent Need more control? Use the FinetuneDB APl to ingest logs and build your own integrations Typed SDKs that capture production data and send it fully async to FinetuneDB. You have full control on what is sent Need more control? Use the FinetuneDB APl to ingest logs and build your ownfrom finetunedb.com
In the maker’s words, at launch
Hey HN! We’re building FinetuneDB (https://finetunedb.com/), an LLM fine-tuning platform. It enables teams to easily create and manage high-quality datasets, and streamlines the entire workflow from fine-tuning to serving and evaluating models with domain experts. You can check out our docs here…
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Finetune LLaMA-7B on commodity GPUs using your own text2023 · github.com · ▲449I've been playing around with https://github.com/zphang/minimal-llama/ and https://github.com/tloen/alpaca-lora/blob/main/finetune.py, and wanted to create a simple UI where you can just paste text, tweak the parameters, and finetune the model quickly using a modern GPU. To prepare the data, simply separate your text with two blank lines. There's an inference tab, so you can test how the tuned model behaves. This is my first foray into the world of LLM finetuning, Python, Torch, Transformers, LoRA, PEFT, and Gradio. Enjoy!
finetune LLMs via the Finetuning Hub2023 · github.com · ▲80Hi HN community, I have been working on benchmarking publicly available LLMs these past couple of weeks. More precisely, I am interested on the finetuning piece since a lot of businesses are starting to entertain the idea of self-hosting LLMs trained on their proprietary data rather than relying on third party APIs. To this point, I am tracking the following 4 pillars of evaluation that businesses are typically look into: - Performance - Time to train an LLM - Cost to train an LLM - Inference (throughput / latency / cost per token) For each LLM, my aim is to benchmark them for…
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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:…
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