How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Phonepadith/aidc-5k-lao-gemma-3n-e4b-it:F16# Run inference directly in the terminal:
llama cli -hf Phonepadith/aidc-5k-lao-gemma-3n-e4b-it:F16Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Phonepadith/aidc-5k-lao-gemma-3n-e4b-it:F16# Run inference directly in the terminal:
./llama-cli -hf Phonepadith/aidc-5k-lao-gemma-3n-e4b-it:F16Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Phonepadith/aidc-5k-lao-gemma-3n-e4b-it:F16# Run inference directly in the terminal:
./build/bin/llama-cli -hf Phonepadith/aidc-5k-lao-gemma-3n-e4b-it:F16Use Docker
docker model run hf.co/Phonepadith/aidc-5k-lao-gemma-3n-e4b-it:F16Quick Links
A newer version of this model is available: Phonepadith/Laollm
🧠 Lao Summarization Model ສະຫລຸບເນື້ອຫາສຳລັບພາສາລາວ - Fine-tuned Gemma 3N-E4B-IT
This is a Lao language summarization model fine-tuned on the Phonepadith/laos_word_dataset, using the base model google/gemma-3-4b-it. The model is designed to generate concise summaries from Lao language text.
📌 Model Details
- Base Model:
google/gemma-3n-e4b-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:
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "Phonepadith/aidc-5k-lao-gemma-3n-e4b-it" # 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)
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf Phonepadith/aidc-5k-lao-gemma-3n-e4b-it:F16# Run inference directly in the terminal: llama cli -hf Phonepadith/aidc-5k-lao-gemma-3n-e4b-it:F16