GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
Keanual
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
Keanual
Model Tree
Qwen/Qwen3-4B
Input Modalities
Output Modalities
Supported Functionality
| Prompt | MiRAI |
|---|---|
| Who are you? | I'm MiRAI, a small multilingual AI assistant that speaks English, Indonesian, and Japanese. |
| Kamu siapa? | Aku MiRAI, asisten AI multibahasa yang bisa berbicara bahasa Inggris, Indonesia, dan Jepang. |
| あなたは誰ですか？ | 私はMiRAIです。英語、インドネシア語、日本語で話せる多言語AIアシスタントです。 |
python
from transformers import AutoTokenizer, AutoModelForCausalLM name = "Keanual/MiRAI-4B"tok = AutoTokenizer.from_pretrained(name)model = AutoModelForCausalLM.from_pretrained(name, torch_dtype="auto", device_map="auto") msgs = [{"role": "user", "content": "Kamu siapa?"}]inp = tok.apply_chat_template(msgs, add_generation_prompt=True, enable_thinking=False, return_tensors="pt", return_dict=True).to(model.device)out = model.generate(**inp, max_new_tokens=256, temperature=0.7, top_p=0.9, do_sample=True)print(tok.decode(out[0][inp["input_ids"].shape[1]:], skip_special_tokens=True))yahma/alpaca-cleanedFreedomIntelligence/alpaca-gpt4-indonesianFreedomIntelligence/alpaca-gpt4-japaneseThe model weights follow the base model license (Apache 2.0). Check the licenses of the datasets listed above before commercial use.
Part of the K-AI model family.
MiRAI is built by K-AI, an Indonesian AI Startup. Learn more about K-AI, our team, and our other projects on our website: kai-official.netlify.app
MiRAI-4B adalah model dari keluarga K-AI: asisten AI multibahasa (Inggris, Indonesia, Jepang) hasil fine-tuning dari Qwen3-4B. Ini versi awal (v0.1): ukurannya kecil, jadi bisa keliru dan jawabannya belum selalu sempurna. Jangan dipakai untuk keputusan medis, hukum, atau keuangan.
from transformers import AutoTokenizer, AutoModelForCausalLM name = "Keanual/MiRAI-4B"tok = AutoTokenizer.from_pretrained(name)model = AutoModelForCausalLM.from_pretrained(name, torch_dtype="auto", device_map="auto") msgs = [{"role": "user", "content": "Kamu siapa?"}]inp = tok.apply_chat_template(msgs, add_generation_prompt=True, enable_thinking=False, return_tensors="pt", return_dict=True).to(model.device)out = model.generate(**inp, max_new_tokens=256, temperature=0.7, top_p=0.9, do_sample=True)print(tok.decode(out[0][inp["input_ids"].shape[1]:], skip_special_tokens=True))