FineTuna

What it does

I've built a UI to speed up dataset building after realizing how tedious it can be. I need external feedback to see if this app can help others :) If you're interested in trying it for free, use this tester code: 593160

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  FineTuna2024 · finetuna-ui.notion.site · ▲6

  Web UI for GPT-4o dataset preparation

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  FineTuner2025 · nowhiteboards.io · ▲164

  NEW88 là nền tảng giải trí trực tuyến uy tín hàng đầu Châu Á với đa dạng sản phẩm, ưu đãi khủng và trải nghiệm an toàn bảo mật trên mọi thiết bị.

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  FinetuneDB – AI fine-tuning platform to create custom LLMs2024 · finetunedb.com · ▲152

  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…

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  Finetune LLaMA-7B on commodity GPUs using your own text2023 · github.com · ▲449

  I'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!

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  prompteasy.ai2024 · prompteasy.ai · ▲196

  Create datasets to fine-tune gpt in under 5min

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  finetune LLMs via the Finetuning Hub2023 · github.com · ▲80

  Hi 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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