TuneKit

What it does

Fine-tuning SLMs the way I wish it worked. I got tired of the fine-tuning setup nightmare, so I built TuneKit: upload your data, get a notebook, and train free on Colab. No GPUs to rent. No scripts to write. No cost. Just results. Try it out at https://tunekit.app/ or check out the code on GitHub at https://github.com/riyanshibohra/TuneKit. Free and open source: let me know if it's useful!

No coding. No guesswork. No infrastructure costs. Upload your data and get a production-ready model in under 15 minutes. Drop your JSONL file. We validate format and analyze patterns automatically. AI recommends the best model and hyperparameters for your specific task. One-click Colab notebook. Hit "Run All" and get your model in 15 minutes. Powered by Unsloth optimization. Train models in half the time on free GPUs. Train on Google Colab's free T4 GPU. No credit card or setup required. AI analyzes your data and recommends the best model for your task. GGUF for Ollama, merged weights for HuggingFace, or LoRA adapters. Best for classification, extraction & structured output. Excellent…from tunekit.app

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  Do 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…

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  Hello all! Very happy to share this toolkit that allows you to fine-tune your choice of open-source LLMs on your data! The toolkit also allows you to run ablation studies across LLMs, prompt designs, training configurations, and can ingest different data files -- all through just ONE YAML file! After fine-tuning, you can also run a bunch of tests to ensure that the fine-tuned LLM behaves as expected, enabling faster time-to-production! Why this toolkit? Why now? While closed-source LLMs have become popular for chat-based applications, enterprises are considering a shift to self-hosted SLMs…

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