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NepaliGPT – Open-Source Nepali and English Language Model (MIT

Hacker News

NepaliGPT – Open-Source Nepali and English Language Model (MIT

I’m excited to share NepaliGPT, an open-source language model tailored for both Nepali and English, finetuned on high-quality Nepali datasets and built on Llama 3.1-8B-Instruct. It’s the first robust, freely available generative AI designed for Nepali, released under the MIT license. Why? Nepali is a low-resource language in the NLP world: foundational tools and models are scarce, and existing solutions are often closed or commercial. I built NepaliGPT to fill this gap and support open research, local application development, and broader innovation. How does it work? Finetuned with Hugging Face TRL & Unsloth for efficiency Supports Nepali and English for tasks like translation, summarization, code generation, and Q&A Built on Llama 3.1-8B-Instruct for strong baseline compatibility and performance Features: 1.MIT license: completely free for research and commercial use 2.Easy integration: Hugging Face compatible, safetensors available. 3.Excel in Nepali language than exiting models. 4.Sample outputs and usage docs included. Collaborate: I’m looking for feedback from linguists, devs, educators, and AI researchers Know of high-quality Nepali datasets or have real-world use cases? Let’s connect Contributors welcome for evaluations, dataset improvements, or downstream apps I hope NepaliGPT helps empower the open-source NLP ecosystem for Nepali and inspires similar work for low-resource languages. Looking forward to your feedback, questions, and ideas for partnership!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, models · Missing: mac, agents, macos
92%92% 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: supports, compatible · Missing: reddit linkedin, podcasting, created
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, lua · Missing: https docs, just released, open source
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, 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 · Strong signals: collaborate · 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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