burmese_tutor is an open-source Burmese tutor built to make learning more accessible. It turns pasted study material into step-by-step lessons, runs locally without internet in the offline setup, and combines practical tutoring behavior with a transparent AI4Burmese runtime.burmese_tutor is an open-source Burmese tutor built to make learning more accessible. It turns pasted study material into step-by-step lessons, runs locally without internet in the offline setup, and combines practical tutoring behavior with a transparent AI4Burmese runtime.
Padauk is not just another Burmese text generator. It is a Burmese-first agentic LLM based on Gemma 4 and specialized with a custom xLAM-format dataset for complex Burmese intent understanding and tool use, distributed through a primary Ollama-ready release and original fine-tuned weights on Hugging Face.Padauk is not just another Burmese text generator. It is a Burmese-first agentic LLM based on Gemma 4 and specialized with a custom xLAM-format dataset for complex Burmese intent understanding and tool use, distributed through a primary Ollama-ready release and original fine-tuned weights on Hugging Face.
A tutorial for AI engineers who want to move beyond model training and deploy a fine-tuned LLM on a custom VM. Uses Padauk as a small-language-model example and walks through VM sizing, model packaging, llama.cpp serving, systemd, reverse proxying, health checks, and production tuning.AI engineer တွေအတွက် လက်တွေ့ကျတဲ့ tutorial တစ်ခုဖြစ်ပြီး fine-tuned LLM ကို custom Ubuntu VM ပေါ်မှာ production-style inference service အဖြစ် deploy လုပ်နည်းကို ပြထားသည်။ Padauk ကို small model example အဖြစ်ယူပြီး VM sizing, GGUF packaging, llama.cpp serving, systemd, reverse proxy, health checks, နဲ့ tuning ကို ရှင်းပြထားသည်။
Dr. Wai Yan Nyein Naing's Burmese-Coder-4B model is featured on HackerNoon, highlighting its role as the first dedicated coding LLM for Myanmar. This 4B parameter model supports Burmese programming prompts and is optimized for local execution via GGUF and MLX, representing a significant advancement in low-resource language AI.Dr. Wai Yan Nyein Naing's Burmese-Coder-4B model is featured on HackerNoon, highlighting its role as the first dedicated coding LLM for Myanmar. This 4B parameter model supports Burmese programming prompts and is optimized for local execution via GGUF and MLX, representing a significant advancement in low-resource language AI.
Burmese-Coder-4B is a 4B Burmese coding assistant by Dr. Wai Yan Nyein Naing, adapted from the Gemma-3 4B family via supervised fine-tuning on Burmese MBPP (974 tasks) and a DPO alignment stage to reduce mixed-language drift. Evaluation uses a two-track pipeline (Pass@1 + LLM-as-a-judge rubric scoring with Gemini 2.5 and DeepSeek V3) as described in the technical whitepaper.Burmese-Coder-4B is a 4B Burmese coding assistant by Dr. Wai Yan Nyein Naing, adapted from the Gemma-3 4B family via supervised fine-tuning on Burmese MBPP (974 tasks) and a DPO alignment stage to reduce mixed-language drift. Evaluation uses a two-track pipeline (Pass@1 + LLM-as-a-judge rubric scoring with Gemini 2.5 and DeepSeek V3) as described in the technical whitepaper.
Dr. Wai Yan Nyein Naing surveys the current landscape of Burmese-language AI in 2025, covering open-source models like Burmese GPT and Burmese-Coder-4B, benchmark evaluation challenges, and practical deployment pathways for Myanmar's low-resource context.Dr. Wai Yan Nyein Naing surveys the current landscape of Burmese-language AI in 2025, covering open-source models like Burmese GPT and Burmese-Coder-4B, benchmark evaluation challenges, and practical deployment pathways for Myanmar's low-resource context.
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