This is a Lao language summarization model fine-tuned on the Phonepadith/laos_word_dataset, using the base model google/gemma-3-12b-it. The model is designed to generate concise summaries from Lao language text. Scope:

📚 ສະຫລຸບຂ່າວ
📚 ສະຫລຸບເອກະສານພາກລັດ
📚 ສະຫລຸບກອງປະຊຸມ

🧠 Lao AIDC-10K Fine-tuned Gemma-3-12B-IT-V2

Model ID: Phonepadith/aidc-llm-laos-10k-gemma-3-12b-it-v2
Base Model: google/gemma-3b-it
Fine-tuned By: Phonepadith Phoummavong

📌 Model Description

This model is a fine-tuned version of Gemma-3-12B-IT-, specifically adapted to understand and generate responses in Lao language 🇱🇦. It was trained using a curated dataset of over 5,000 high-quality Lao input-output pairs, primarily focused on AIDC (Artificial Intelligence and Digital Content) topics.

Key Features:

🗣️ Fine-tuned for Lao language generation
📚 Suitable for summarization, question answering, general chat
🧠 Based on Google's powerful Gemma 3-12B Instruct model

🧾 Training Details

Detail	Value
Base Model	Gemma 3-12B Instruct
Fine-tuning Method	LoRA with PEFT (Unsloth)
Dataset	10,000 Laos supervised samples
Sequence Length	2048
Batch Size	2 (with gradient accumulation)
Optimizer	AdamW
Epochs	3–5 (early stopping enabled)
Format	GGUF (F16, Q8_0, Q4_0 available)

📥 How to Use (LM Studio)

Install LM Studio: https://lmstudio.ai
Import the Model:
- Via Hugging Face: Search for Phonepadith/aidc-llm-laos-10k-gemma-3-12b-it
- Or drag the .gguf file into LM Studio
Set System Prompt:

📌 Model Details

Base Model: google/gemma-3-12b-it
Fine-tuned by: Phonepadith
Language: Lao (lo)
Task: Text Generation
Library: adapter-transformers
License: Apache 2.0

📊 Metrics

Evaluation Metric: BLEU score
BLEU is used to evaluate the quality of generated summaries against reference summaries in the dataset.

🛠️ How to Use

You can load and use the model with Hugging Face Transformers and adapter-transformers:

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Phonepadith/aidc-llm-laos-10k-gemma-3-12b-it-v2"  # change to your actual model name
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

input_text = "ປັດຈຸບັນ ກອງທັບປະຊາຊົນລາວ ມີການປະກອບວັດຖຸເຕັກນິກທັນສະໄໝສົມຄວນ, ສາມາດຕອບສະໜອງ ໃຫ້ແກ່ວຽກງານປ້ອງກັນຊາດ ໃນໄລຍະໃໝ່ ໄດ້ໂດຍພື້ນຖານ; ໄດ້ປະກອບສ່ວນຢ່າງຕັ້ງໜ້າເຂົ້າໃນການປ້ອງກັນ, ຄວບຄຸມໄພພິບັດ ແລະ ຊ່ວຍເຫລືອປະຊາຊົນ ຜູ້ປະສົບໄພພິບັດທຳມະຊາດຕ່າງໆທີ່ເກີດຂຶ້ນໃນຂອບເຂດທົ່ວປະເທດ. ພ້ອມນັ້ນ, ກໍໄດ້ເປັນເຈົ້າການປະກອບສ່ວນປັບປຸງກໍ່ສ້າງພື້ນ ຖານການເມືອງ, ກໍ່ສ້າງທ່າສະໜາມສົງຄາມປະຊາຊົນ 3 ຂັ້ນ ຕິດພັນກັບວຽກງານ 3 ສ້າງ ຢູ່ທ້ອງຖິ່ນຕາມ 4 ເນື້ອໃນ 4 ຄາດໝາຍ ແລະ ສືບທອດມູນເຊື້ອຄວາມສາມັກຄີ ກັບກອງທັບປະເທດເພື່ອນມິດ ສາກົນ, ປະຕິບັດນະໂຍບາຍເພີ່ມມິດຫລຸດຜ່ອນສັດຕູ, ຮັບປະກັນສະຖຽນລະພາບ ຂອງລະບອບການ ເມືອງ, ຮັກສາຄວາມສະຫງົບປອດໄພຕາມຊາຍແດນ"
inputs = tokenizer(input_text, return_tensors="pt")
summary_ids = model.generate(**inputs, max_new_tokens=100)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

print(summary)