Instructions to use arnelirobles/abogado with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arnelirobles/abogado with Transformers:

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

pipe = pipeline("text-generation", model="arnelirobles/abogado")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arnelirobles/abogado")
model = AutoModelForCausalLM.from_pretrained("arnelirobles/abogado")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use arnelirobles/abogado with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/arnelirobles/abogado

SGLang

How to use arnelirobles/abogado 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 "arnelirobles/abogado" \
    --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": "arnelirobles/abogado",
		"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 "arnelirobles/abogado" \
        --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": "arnelirobles/abogado",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arnelirobles/abogado with Docker Model Runner:
```
docker model run hf.co/arnelirobles/abogado
```

⚖️ Abogado — Open Source Philippine Law AI

Abogado (Filipino for "Lawyer") is an open-source AI assistant fine-tuned to help Filipinos understand Philippine law. It is specifically designed for barangay officials, local councilors, mediators, and ordinary citizens who need accessible legal information.

⚠️ Disclaimer: Abogado is NOT a lawyer. It provides legal information for educational purposes only. Always consult a licensed Philippine attorney for actual legal advice. For free legal assistance, contact the Public Attorney's Office (PAO) or IBP legal aid.

Why Abogado?

Most AI models are trained on US/UK law and perform poorly on Philippine law
Millions of barangay officials handle legal disputes daily without formal legal training
Access to legal information in the Philippines is expensive and inaccessible for many
Abogado aims to democratize legal knowledge for every Filipino

Model Details

Base Model: Qwen/Qwen2.5-3B-Instruct
Method: QLoRA fine-tuning (4-bit quantization, LoRA rank 16)
Training Data: 106 Q&A pairs from the 1987 Philippine Constitution
Languages: English and Filipino/Tagalog
License: Apache 2.0 (fully open source)
Hardware: Kaggle T4 GPU

What It Knows

Currently trained on:

✅ 1987 Philippine Constitution (all 18 Articles)
✅ Bill of Rights practical applications
✅ Katarungang Pambarangay (Barangay Justice System)
✅ VAWC (RA 9262) basics and Barangay Protection Orders
✅ Local government structure and powers
✅ Rights of arrested persons
✅ Safety behaviors (refuses private data, recommends real lawyers)

Planned future training data:

📋 Local Government Code (RA 7160)
📋 Revised Penal Code
📋 Family Code
📋 Labor Code
📋 RA 9262 (VAWC) full text
📋 RA 7610 (Child Protection)
📋 Supreme Court jurisprudence
📋 Rules on Summary Procedure

How to Use

With Transformers (Python)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("YOUR_USERNAME/abogado")
tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/abogado")

messages = [
    {"role": "system", "content": "You are Abogado, an open-source Philippine law assistant."},
    {"role": "user", "content": "Can a barangay captain issue a warrant of arrest?"},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With Ollama (Local/Docker)

# Download the GGUF version and create a Modelfile, then:
ollama create abogado -f Modelfile
ollama run abogado "What is the Katarungang Pambarangay?"

Target Users

🏘️ Barangay Officials — Captains, Kagawads, Lupon members handling disputes
🏛️ Local Councilors — Municipal/city council members drafting ordinances
🤝 Mediators — Lupong Tagapamayapa members conducting conciliation
📚 Law Students — For study and bar exam review
🇵🇭 Filipino Citizens — Anyone who wants to understand their rights

Safety Features

Abogado is trained to:

❌ Refuse to review actual case documents or provide specific legal advice
⚠️ Warn users when they share personal or confidential information
✅ Redirect users to licensed attorneys, PAO, or IBP legal aid
📋 Always include a disclaimer that information is for educational purposes only

Limitations

Currently trained only on the Philippine Constitution (106 Q&A pairs)
May produce inaccurate or incomplete legal information
Cannot replace professional legal advice
Knowledge is limited to training data — does not have access to the latest laws or jurisprudence
Performance will improve significantly as more training data is added

Contributing

Abogado is open source! You can help by:

Adding more training data — Q&A pairs from other Philippine laws
Testing and reporting issues — File issues on the repo
Translating — Adding more Filipino/Tagalog Q&A pairs
Sharing — Tell barangay officials, law students, and communities about Abogado

Training Details

Epochs: 5
Batch size: 2 (with gradient accumulation of 4, effective batch size 8)
Learning rate: 2e-4
Optimizer: AdamW 8-bit
Precision: FP16
LoRA rank: 16
LoRA alpha: 16
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj