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

Open models for indigenous Indonesian languages

Bakpia

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.

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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](https://huggingface.co/afrizalha/Bakpia-V1-0.5B-Javanese/) | Epoch = 1, Batch = 16\*8, lr = 5e-5, linear schedule| | V1 1.5B | Qwen 2 1.5B Instruct | [fp16](https://huggingface.co/afrizalha/Bakpia-V1-1.5B-Javanese) | Epoch = 1, Batch = 16\*8, lr = 5e-5, linear schedule| | V1 9B | Gemma 2 9B Instruct | [fp16](https://huggingface.co/afrizalha/Bakpia-V1-9B-Javanese-fp16)/[4bit](https://huggingface.co/afrizalha/Bakpia-V1-9B-Javanese-4bit/) |Batch size = 16\*8, lr = 4e-5, linear schedule| Training data is accessible [here](https://huggingface.co/datasets/afrizalha/Gatra-2-Javanese). ## 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 = """user {prompt} 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