Instructions to use ai-literacy-innovation-institute/ilma-kutub-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ai-literacy-innovation-institute/ilma-kutub-6 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("ai-literacy-innovation-institute/ilma-kutub-6") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use ai-literacy-innovation-institute/ilma-kutub-6 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "ai-literacy-innovation-institute/ilma-kutub-6"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ai-literacy-innovation-institute/ilma-kutub-6" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use ai-literacy-innovation-institute/ilma-kutub-6 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ai-literacy-innovation-institute/ilma-kutub-6"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ai-literacy-innovation-institute/ilma-kutub-6" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ai-literacy-innovation-institute/ilma-kutub-6", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use ai-literacy-innovation-institute/ilma-kutub-6 with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "ai-literacy-innovation-institute/ilma-kutub-6"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default ai-literacy-innovation-institute/ilma-kutub-6
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ai-literacy-innovation-institute/ilma-kutub-6 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "ai-literacy-innovation-institute/ilma-kutub-6"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "ai-literacy-innovation-institute/ilma-kutub-6" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
ILMA-KUTUB6
Qwen2.5-7B-Instruct — fine-tuned with LoRA on Kutub al-Sittah hadith dataset
Model fine-tuning pertama dari AI Literacy Innovation Institute (ALII) UIN Syarif Hidayatullah Jakarta. Dikembangkan untuk analisis hadits dalam bahasa Indonesia dan Arab.
Training Performance
| Metric | Value |
|---|---|
| Final Train Loss | 0.193 |
| Final Val Loss | 0.183 |
| Total Tokens Trained | 223,977 |
| Training Time | ~54 menit (500 iter) |
| Peak Memory | 10.176 GB |
Cara Penggunaan
1. Install Dependencies
pip install mlx-lm
2. Load Model
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler
model, tokenizer = load("ai-literacy-innovation-institute/ilma-kutub-6")
3. Generate (Plain Prompt)
response = generate(
model, tokenizer,
prompt="Apa hukum shalat berjamaah?",
max_tokens=1024,
sampler=make_sampler(temp=0.7),
)
print(response)
4. Generate (Chat Template - Recommended)
messages = [
{"role": "system", "content": "Kamu adalah seorang asisten keilmuan Islam..."},
{"role": "user", "content": "Analisis sanad hadits riwayat Bukhari."},
]
formatted = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(
model, tokenizer,
prompt=formatted,
max_tokens=1024,
sampler=make_sampler(temp=0.7),
)
print(response)
5. Conversational Chat
history = [{"role": "system", "content": "Kamu adalah seorang asisten keilmuan Islam..."}]
history.append({"role": "user", "content": "Apa itu hadits mutawatir?"})
formatted = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=formatted, max_tokens=512, sampler=make_sampler(temp=0.7))
history.append({"role": "assistant", "content": response})
print("A:", response)
# Follow-up question
history.append({"role": "user", "content": "Berikan contohnya dari Shahih Bukhari."})
formatted = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=formatted, max_tokens=512, sampler=make_sampler(temp=0.7))
print("A:", response)
6. Streamlit App
streamlit run app.py
Contoh Pertanyaan untuk Testing
Sanad & Rijal
Analisis sanad hadits: حَدَّثَنَا قُتَيْبَةُ بْنُ سَعِيدٍ، حَدَّثَنَا لَيْثٌ، عَنِ ابْنِ شِهَابٍ، عَنْ عُرْوَةَ، عَنْ عَائِشَةَ. Identifikasi setiap perawi beserta thabaqat-nya dan kualitas sanad.
Bandingkan metode al-Jarh wa al-Ta'dil antara Imam Ibnu Hajar dalam Tahdhib al-Tahdhib dengan Imam al-Dhahabi dalam Mizan al-I'tidal.
Seorang perawi dinilai majhul. Jelaskan perbedaan pendapat ulama tentang penerimaan riwayat perawi majhul.
Mustalah al-Hadits
Jelaskan perbedaan hadits shahih li-dzatihi dan shahih li-ghairihi. Berikan contoh dari Shahih al-Bukhari.
Apa kriteria Imam al-Tirmidzi dalam menetapkan status hasan? Bandingkan dengan pendekatan Ibnu Hajar.
Apa itu hadits mudraj? Berikan contoh dari Sunan al-Tirmidzi dan cara mendeteksinya.
Takhrij & Perbandingan Kutub al-Sittah
Lakukan takhrij hadits "من سن في الإسلام سنة حسنة" — bandingkan lafaz riwayat antara Muslim, al-Tirmidzi, dan Ibnu Majah.
Hadits "إنما الأعمال بالنيات" — bandingkan seluruh riwayat dalam Kutub al-Sittah. Adakah perbedaan redaksi antar kitab?
Mengapa hadits "لا ضرر ولا ضرار" hanya diriwayatkan oleh Ibnu Majah? Bagaimana statusnya sebagai dalil hukum?
Fiqh & Hadits
Terdapat dua hadits yang tampak kontradiktif: (a) Puasa Syawwal seperti puasa setahun (HR. Muslim) dan (b) Tidak ada puasa setelah Ramadhan kecuali sunnah (HR. Bukhari). Bagaimana ulama memadukan keduanya?
Hadits mu'an'an — apa syarat Imam al-Bukhari dan Muslim dalam menerimanya? Mengapa Syu'bah menolak hadits mu'an'an dari mudallis?
Multi-dimensi
Analisis hubungan QS. al-Maidah [5]: 3 dengan hadits "بني الإسلام على خمس". Jelaskan korelasi ayat ikmal al-din dengan rukun Islam.
Jika sebuah hadits muttafaq 'alaih ternyata memiliki 'illah yang baru terdeteksi — bagaimana sikap peneliti?
Bandingkan pendekatan Sunan Abu Dawud vs Ibnu Majah dalam meriwayatkan hadits ahkam. Mana yang lebih ketat?
Peneliti & Akademik
Apa itu AI Literacy Innovation Institute (ALII) dan apa fokus utamanya?
Siapa Dr. Kamal Fiqry Musa dan apa kontribusinya dalam pengembangan AI untuk kajian kitab kuning?
Dataset
Dataset SFT (60,561 examples) mencakup analisis hadits dari Kutub al-Sittah, kritik sanad, biografi perawi, fiqh, dan profil peneliti ALII UIN Jakarta.
License
Apache 2.0
Citation
@misc{ilma-kutub6-2026,
author = {ALII UIN Jakarta},
title = {ILMA-KUTUB6: Qwen2.5-7B Fine-tuned on Kutub al-Sittah},
year = {2026},
publisher = {AI Literacy Innovation Institute},
}
Dikembangkan oleh AI Literacy Innovation Institute (ALII) — UIN Syarif Hidayatullah Jakarta
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