cali-id-en-translate

CALI Indonesian-English Translation Model

A lightweight 121M-parameter translation model fine-tuned from Sandroeth/cali-0.1B for bilingual translation between Indonesian and English.

Capabilities

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:

[id→en]
{input text}
→

English → Indonesian:

[en→id]
{input text}
→

Usage

Inference example:

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

Citation

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

@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