metadata

language:
  - jv
license: apache-2.0
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - gemma2
  - trl
  - sft
datasets:
  - afrizalha/Gatra-2-Javanese

Open models for indigenous Indonesian languages

Bakpia is a family of open language models capable of responding in Javanese language. Version one of Bakpia is the first generative Javanese LLM gain functional instruction performance using solely synthetic data.

Beta preview

Bakpia V1 is a family of Javanese language models. It is fine-tuned from available open models using massive synthetic data for Krama Javanese, where the prompts are generated by GPT-4o and the responses are generated by Claude 3 Haiku.

This repository contains the 4bit version of Bakpia V1 9B.

Version	Base Model	URL	Training
V1 0.5B	Qwen 2 0.5B Instruct	fp16	Epoch = 1, Batch = 16*8, lr = 5e-5, linear schedule
V1 1.5B	Qwen 2 1.5B Instruct	fp16	Epoch = 1, Batch = 16*8, lr = 5e-5, linear schedule
V1 9B	Gemma 2 9B Instruct	fp16/4bit	Batch size = 16*8, lr = 4e-5, linear schedule

Training data is accessible here.

Version 1.0

This is the first version of Bakpia.

✨ Training

36K input-output pairs
64/128 lora r/alpha
Rank-stabilized lora

✨ Features

Single-turn QA across various domains.
Ngoko Javanese not currently supported.

Generate with template

# Update transformers for Gemma 2 compatibility + install accelerate and bitsandbytes for loading 4bit model 
!pip install -q git+https://github.com/huggingface/transformers.git
!pip install -q accelerate bitsandbytes

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

tokenizer = AutoTokenizer.from_pretrained("afrizalha/Bakpia-V1-9B-Javanese-4bit")
model = AutoModelForCausalLM.from_pretrained("afrizalha/Bakpia-V1-9B-Javanese-4bit", quantization_config=BitsAndBytesConfig(load_in_4bit=True))
model.to("cuda")

template = """<start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model
"""

input = template.format(prompt="Kados pundi kulo saged nyinaoni Basa Jawa kanthi sae?")
input = tokenizer([input], return_tensors = "pt").to("cuda")
outputs = model.generate(**input, max_new_tokens = 1024, streamer= TextStreamer(tokenizer), temperature=.5, use_cache=True, do_sample=True)

Acknowledgments

Developed by: Afrizal Hasbi Azizy
License: Apache-2.0