LLM fine-tuning without infra or ML expertise
Fine-tune large language models with your own data. Simple, powerful, and affordable.
LLM fine-tuning without infra or ML expertise launched on January 21, 2026 with 5 votes, #3,400 of 14,584 launches that month and more than 75% of that year's launches.
What it does
Upload your data, pick a model, and get a custom AI that understands your domain. No ML expertise required. No infrastructure to manage. Select from Gemma, Qwen, Mistral, and other popular open-source models. Pick the size that fits your needs. Upload CSV, JSON, or JSONL files with your training examples. We'll automatically detect the format. Download your model or deploy to Hugging Face with one click. Ready to use anywhere. Using LoRA, your models are ready in minutes instead of hours or days. Your data is never shared or used to train other models. Fully private. Download your model and deploy it anywhere. No vendor lock-in. Push to Hugging Face with one click, or download and run…from tinytune.xyz
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Fine-tune an 8B model on a 4 GB laptop GPUAug 2026 · github.com · ▲139Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU. - MakazhanAlpamys/Soup
TuneTrain.ai2025 · tunetrain.ai · ▲79Do you know that Small Language Models (SLM) can outperform LLMs if trained (fine-tuned) on domain specific datasets, like your companies knowledge base? The issue: fine-tuning a SLM usually requires a dataset of 10k - 100k records, which is huge. I created a platform for applying "human in the loop" augmentation techniques on your small dataset, so that you can start with maybe 100 records and build-up quickly huge datasets and launch a training without prior knowledge. I implemented 2 techniques, based on LLM distillation, however: more to come. HN, what Do you think? Do you see value in…
FinetuneDB – AI fine-tuning platform to create custom LLMs2024 · finetunedb.com · ▲152Hey 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…
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!
Fine-grained stylistic control of LLMs using model arithmetic2023 · github.com · ▲85We developed a new framework that enables flexible control of generated text in language models. By combining several models and/or system prompts in one mathematical formula, it lets you tweak your style and combine model outputs with ease. A handy tool for those working with LLMs, looking for more fine-grained control of stylistic output. More details in our paper: https://arxiv.org/abs/2311.14479. Feedback and potential applications are welcome.
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