Instructions to use icaluwu/Llama-HellanodikAI-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use icaluwu/Llama-HellanodikAI-8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="icaluwu/Llama-HellanodikAI-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("icaluwu/Llama-HellanodikAI-8B")
model = AutoModelForCausalLM.from_pretrained("icaluwu/Llama-HellanodikAI-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use icaluwu/Llama-HellanodikAI-8B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="icaluwu/Llama-HellanodikAI-8B",
	filename="llama3-8b-cpt-sahabatai-v1-instruct.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use icaluwu/Llama-HellanodikAI-8B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf icaluwu/Llama-HellanodikAI-8B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf icaluwu/Llama-HellanodikAI-8B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf icaluwu/Llama-HellanodikAI-8B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf icaluwu/Llama-HellanodikAI-8B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf icaluwu/Llama-HellanodikAI-8B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf icaluwu/Llama-HellanodikAI-8B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf icaluwu/Llama-HellanodikAI-8B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf icaluwu/Llama-HellanodikAI-8B:Q4_K_M

Use Docker

docker model run hf.co/icaluwu/Llama-HellanodikAI-8B:Q4_K_M

LM Studio
Jan

vLLM

How to use icaluwu/Llama-HellanodikAI-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "icaluwu/Llama-HellanodikAI-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "icaluwu/Llama-HellanodikAI-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/icaluwu/Llama-HellanodikAI-8B:Q4_K_M

SGLang

How to use icaluwu/Llama-HellanodikAI-8B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "icaluwu/Llama-HellanodikAI-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "icaluwu/Llama-HellanodikAI-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "icaluwu/Llama-HellanodikAI-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "icaluwu/Llama-HellanodikAI-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use icaluwu/Llama-HellanodikAI-8B with Ollama:
```
ollama run hf.co/icaluwu/Llama-HellanodikAI-8B:Q4_K_M
```

Unsloth Studio

How to use icaluwu/Llama-HellanodikAI-8B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for icaluwu/Llama-HellanodikAI-8B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for icaluwu/Llama-HellanodikAI-8B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for icaluwu/Llama-HellanodikAI-8B to start chatting

Atomic Chat new
Docker Model Runner
How to use icaluwu/Llama-HellanodikAI-8B with Docker Model Runner:
```
docker model run hf.co/icaluwu/Llama-HellanodikAI-8B:Q4_K_M
```

Lemonade

How to use icaluwu/Llama-HellanodikAI-8B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull icaluwu/Llama-HellanodikAI-8B:Q4_K_M

Run and chat with the model

lemonade run user.Llama-HellanodikAI-8B-Q4_K_M

List all available models

lemonade list

Llama-HellanodikAI-8B

HellanodikAI is a Llama-3 8B model fine-tuned (LoRA) from GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct for the Indonesian legal/educational domain. It is trained and released for educational and research purposes, and is not a substitute for professional legal advice.

Developed by: Teuku Vaickal Rizki Irdian (icaluwu.site)
Model type: Causal LM, LoRA fine-tune of Llama 3 8B
Language(s): Indonesian (primary), English
License: CC BY-NC 4.0 with additional terms — see LICENSE.md
Finetuned from model: GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct

This model was trained 2x faster with Unsloth and Hugging Face TRL.

⚠️ Intended Use & Limitations

HellanodikAI is built strictly for educational and informational purposes — for example, learning about Indonesian legal concepts, drafting practice exercises, or academic research on legal-domain LLMs.

Do NOT use this model to:

Generate legally binding advice, opinions, or decisions
Replace consultation with a licensed advocate/notary/legal professional
Make decisions with real legal, financial, or personal consequences without human expert review

The model can produce incorrect, outdated, or incomplete legal information ("hallucinations"). Indonesian law changes over time (new UU, PP, Perpres, putusan MA/MK) and this model's knowledge is frozen at training time — always verify against primary legal sources (JDIH, peraturan.go.id, putusan resmi pengadilan) before relying on any output.

Model Details


Base model	GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct (Llama 3 8B, ID-adapted)
Fine-tuning method	LoRA (PEFT)
LoRA rank (r)	16
LoRA alpha	32
LoRA dropout	0.05
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training framework	Unsloth + TRL (SFTTrainer)
Hardware	Kaggle free-tier T4 GPU

Training Data

Dataset details to be added.

Files & Usage

This repository provides two artifacts:

File	Format	Use case
`adapter_model.safetensors` + `adapter_config.json`	LoRA adapter (PEFT)	Load on top of the base model with `transformers` + `peft` for further fine-tuning or research
`hellanodikai-8b.Q4_K_M.gguf`	Merged & quantized GGUF	Standalone inference via llama.cpp, Ollama, LM Studio, Jan, etc.

Using the LoRA adapter (transformers + peft)

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct"
adapter = "icaluwu/Llama-HellanodikAI-8B"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

Using the GGUF file (Ollama)

ollama run hf.co/icaluwu/Llama-HellanodikAI-8B:Q4_K_M

Using the GGUF file (llama.cpp)

llama-cli -hf icaluwu/Llama-HellanodikAI-8B:Q4_K_M --jinja

Currently only a Q4_K_M quantization is provided. Additional quant levels (Q5_K_M, Q8_0) may be added in future releases.

Evaluation

No formal evaluation results published yet.

License

This model is licensed under CC BY-NC 4.0 with additional terms — see LICENSE.md for full details.

✅ Free for academic, research, and non-commercial community use, with attribution to Teuku Vaickal Rizki Irdian.
🚫 Commercial use is not permitted without a separate license.
💼 For commercial licensing inquiries, contact Teuku Vaickal Rizki Irdian via LinkedIn or icaluwu.site.
As a derivative of Llama 3, this model is also subject to the Llama 3 Community License. Attribution "Built with Llama" is required per that license.

Citation

If you use this model in academic work, please cite:

@misc{hellanodikai2026,
  author = {Irdian, Teuku Vaickal Rizki},
  title = {HellanodikAI: Indonesian Legal-Domain Llama-3 8B},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/icaluwu/Llama-HellanodikAI-8B}}
}