Streamtune - Fine-Tune LLMs with Zero Code

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While working on a different project for academics, I wanted to fine-tune a language model that provided reviews for their manuscripts. I’ve done some fine-tuning and transfer learning of models in the past, so I thought this process would be really similar. I quickly realized that fine-tuning LLMs is a different beast and every new resource uncovered more questions. How do I actually structure my data? Where do I access powerful GPUs? What is LoRa, what is quantization? What hyperparameters do I use? In this exploration I began building a web app to fine tune LLMs without code. This is what I’ve come up with in the past 3 weeks. - Bring your data as a CSV or JSONL - Create a fine-tune of Llama 3 and your data - Use and evaluate your newly fine-tuned model in app The larger vision is for anyone, technical or not, to be able to come with any data source and an “idea” of what they want to accomplish and in a few hours receive an endpoint to a model. The app is currently open to try out and doesn’t ask for any sign-up. I’ve set rate limits to 2 fine-tunes a day each up to 500 examples and up to 1 hour usage of the model after its created - just to limit costs. I've included three models finetuned with the tool to try out as well: 1. Tweets QA - answer questions about a given tweet 2. Customer Support - professional responses to customer support queries 3. SQL - generate SQL code from natural language I’d appreciate any feedback & questions.

Visit product View on Hacker NewsJul 17, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos

90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: lua, llama, ide · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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