metadata

language:
  - id
  - en
base_model:
  - meta-llama/Llama-3.2-11B-Vision-Instruct
  - openai/whisper-large
tags:
  - multimodal
  - indonesian
  - english
  - vision
  - audio
  - text

LaBahasa 11B

Model Information

LaBahasa 11B is a multimodal LLM that combines text, audio, and image processing capabilities. Built upon OpenAI's Whisper and Meta's Llama 3.2 architectures, this model has been specifically optimized for Indonesian language understanding while maintaining English capability. The model was trained on 9 billion high quality bilingual dataset comprising Indonesian and English speech and text data.

Model Architecture: LaBahasa 11B uses a feed-forward network to project audio embeddings from Whisper Large encoder to Llama's input embeddings, combined with image/text inputs to enable multimodal understanding.

Model Developer: Bahasa AI and LintasArta

Intended Use

This model is intended for various NLP tasks that require text/audio/image understanding and generating Indonesian language.

Usage

Installation

pip install --upgrade pip
pip install --upgrade transformers

Use with Transformers

For audio input, LaBahasa 11B uses a special placeholder token <|audio|>, which then be replaced with the projected audio embedding.

import transformers
import torch
import librosa, requests
from PIL import Image

model = transformers.AutoModel.from_pretrained('LABahasa/llama-labahasa-11B', 
                                             trust_remote_code=True,
                                             torch_dtype=torch.bfloat16,
                                             device_map='cuda')
processor = transformers.AutoProcessor.from_pretrained('LABahasa/llama-labahasa-11B', 
                                                     trust_remote_code=True)

# Example with all modalities
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"
image = Image.open(requests.get(url, stream=True).raw)
audio_path = "deskripsi.mp3"
audio, * = librosa.load(audio_path, sr=22050)

messages = [
    {
        'role': 'system',
        'content': 'You are a helpful AI assistant.'
    },
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "\n<|audio|>\n"},
        ],
    }
]

input_text = processor.tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False
)

inputs = processor(
    images=image,
    text=input_text,
    audio=audio,
    return_tensors="pt",
    sampling_rate=16000,
).to(model.device)

input_len = inputs.input_ids.shape[1]
outputs = model.generate(**inputs, max_new_tokens=100)
print(processor.decode(outputs[0][input_len:]))

Evaluation

Metric	Qwen2.5-14B	llama-labahasa-11B
MMLU	66.3	67.2
Multi-Mathematics	63.7	64.5
MMMU	68.2	68.2
id-MMLU	63.1	72.2

Training Details

Training regime: BF16 mixed precision training

Training Infrastructure: 8xH100 GPUs

Training Time: 25 hours

Training Data

LaBahasa 11B was trained on an extensive 9 billion high quality bilingual dataset comprising Indonesian and English speech and text data.

Training Procedure

LaBahasa 11B was trained on customized training methodology modifications to enhance:

Image input processing capabilities through integration with Llama 3.2's vision features
Indonesian language understanding and generation