nowfound

Alternatives

Products that do what TuneSalon AI does

Turn your data into a custom LLM. No coding required.

  1. 1FL

    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!

    2023 · github.com

  2. 2
    Taylor AI118

    Fine-tune open source LLMs in minutes

    2023

  3. 3
    Arkor142

    Fine-tune and Deploy Open-weight Models in TypeScript

    Jul 2026 · arkor.ai

  4. 4

    Build LLMs powered by GPT & your own data

    2023

  5. 5
    re:tune475

    The missing frontend for GPT-3

    2023

  6. 6
    FineTuner164

    Fine-tune AI models on your data — in minutes, not days.

    2025

  7. 7TL

    Hey HN, we wanted to share our repo where we fine-tuned Llama 3.1 on Google TPUs. We’re building AI infra to fine-tune and serve LLMs on non-NVIDIA GPUs (TPUs, Trainium, AMD GPUs). The problem: Right now, 90% of LLM workloads run on NVIDIA GPUs, but there are equally powerful and more cost-effective alternatives out there. For example, training and serving Llama 3.1 on Google TPUs is about 30% cheaper than NVIDIA GPUs. But developer tooling for non-NVIDIA chipsets is lacking. We felt this pain ourselves. We initially tried using PyTorch XLA to train Llama 3.1 on TPUs, but it was rough: xla…

    2024 · github.com

  8. 88F

    Hi HN! I'm just sharing a project I've been working on during the LLM Efficiency Challenge - you can now finetune Llama with QLoRA 5x faster than Huggingface's original implementation on your own local GPU. Some highlights: 1. Manual autograd engine - hand derived backprop steps. 2. QLoRA / LoRA 80% faster, 50% less memory. 3. All kernels written in OpenAI's Triton language. 4. 0% loss in accuracy - no approximation methods - all exact. 5. No change of hardware necessary. Supports NVIDIA GPUs since 2018+. CUDA 7.5+. 6. Flash Attention support via Xformers. 7. Supports 4bit and 16bit…

    2023 · github.com

  9. 9FA

    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…

    2024 · finetunedb.com

  10. 10

    Fine-tune AI models with your augmented data

    Oct 2025

  11. 11MB

    Hey HN! We're excited to share our new open-source project, Marvin. Marvin is a high-level library for building AI-powered software. We developed it to address the challenges of integrating LLMs into more traditional applications. One of the biggest issues is the fact that LLMs only deal with strings (and conversational strings at that), so using them to process structured data is especially difficult. Marvin introduces a new concept called AI Functions. These look and feel just like regular Python functions: you provide typed inputs, outputs, and docstrings. However, instead of relying on…

    2023 · github.com

  12. 12WM

    Try it out! https://glhf.chat/ Hey HN! We’ve been working for the past few months on a website to let you easily run (almost) any open-source LLM on autoscaling GPU clusters. It’s free for now while we figure out how to price it, but we expect to be cheaper than most GPU offerings since we can run the models multi-tenant. Unlike Together AI, Fireworks, etc, we’ll run any model that the open-source vLLM project supports: we don’t have a hardcoded list. If you want a specific model or finetune, you don’t have to ask us for it: you can just paste the Hugging Face link in and…

    2024 · glhf.chat

  13. 13
    AskCodi230

    Custom LLMs, without training. Use via openai compatible api

    Nov 2025

  14. 14

    Calculate the GPU memory you need for LLM inference

    2025

  15. 15

    Vibe-check many open-source and proprietary LLMs at once

    2024

  16. 16

    AI fine-tuning platform to create custom LLMs

    2024

  17. 17

    Massively multi-player game played by talking to an LLM

    May 2026 · gradient-bang.com

  18. 18

    LLM reinforcement fine-tuning platform to improve LLM output

    2025

  19. 19

    The fast, easy and cheap OpenAI alternative

    2023

  20. 20
    LM Studio209

    Discover, download, and run local LLMs (incl. DeepSeek R1)

    2025

  21. 21OS

    Hi 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.

    2025 · github.com

  22. 22CY

    Hello HN, We built Promptrepo to make finetuning accessible to product teams — not just ML engineers. Last week, OpenAI’s CPO shared how they use fine-tuning for everything from customer support to deep research, and called it the future for serious AI teams. Yet most teams I know still rely on prompting, because fine-tuning is too technical, while the people who have the training data (product managers and domain experts) are often non-technical. With Promptrepo, they can now: - Add training examples in Google Sheets - Click a button to train - Deploy and test instantly - Use OpenAI,…

    2025 · promptrepo.com

  23. 23

    Transform generic AI models into specialized solutions

    2025

  24. 24UT

Ranked by how close each launch is in meaning, then by votes. Refine with a description →