The model performs well on everyday sentences, casual conversations, and general-purpose translation between Indonesian and English. Its small size makes it suitable for applications that require low latency and lightweight deployment.

However, there are some limitations:

Very short inputs (1–2 words) may produce irrelevant translations.
Technical or specialized domains such as machine learning, medicine, or law are less reliable.
Long passages (over approximately 200 words) may gradually drift and lose consistency.

Training Data

Total samples: 179,999 bilingual sentence pairs
Training set: 161,999 samples
Evaluation set: 18,000 samples
Indonesian samples: 89,999
English samples: 90,000
Training epochs: 3
Learning rate: 5e-5

Prompt Format

The model was fine-tuned using a fixed prompt template. For the best results, use the following formats.

Indonesian → English:

text

[id→en]
{input text}
→

English → Indonesian:

text

[en→id]
{input text}
→

Usage

Inference example:

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch, re

model_id = "Sandroeth/cali-id-en-translate"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    device_map="auto"
)
model.eval()

def translate(text, locale):
    direction = "[id→en]" if locale == "id" else "[en→id]"
    prompt = f"{direction}\n{text}\n→"

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        out = model.generate(
            **inputs,
            max_new_tokens=64,
            do_sample=False,
            repetition_penalty=1.5,
            no_repeat_ngram_size=3,
            pad_token_id=tokenizer.eos_token_id,
            use_cache=False,
        )

    gen = out[0][inputs["input_ids"].shape[1]:]
    result = tokenizer.decode(gen, skip_special_tokens=True).strip()

    return re.split(r'(?<=[.!?])\s+', result)[0]

print(translate("Dia pergi ke pasar setiap pagi.", "id"))
print(translate("The weather is very cold today.", "en"))

Example Outputs

Input	Direction	Output
Saya makan nasi.	id→en	I eat rice.
Dia pergi ke pasar setiap pagi.	id→en	He goes to the market every morning.
She is happy.	en→id	Dia bahagia.
The weather is very cold today.	en→id	Cuacanya sangat dingin hari ini.
Pemerintah sedang membangun infrastruktur baru.	id→en	The government is building new infrastructure.

Citation

If you use or reference this model in your research or projects, please cite:

bibtex

@article{cali2026,
  title        = {CALI 0.1B},
  author       = {Sandroeth},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/Sandroeth/cali-0.1B}
}

Author

Sandroeth

License

Apache License 2.0