Instructions to use MaAIos/Liyama-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaAIos/Liyama-3B with Transformers:

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

pipe = pipeline("text-generation", model="MaAIos/Liyama-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaAIos/Liyama-3B")
model = AutoModelForCausalLM.from_pretrained("MaAIos/Liyama-3B")
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 MaAIos/Liyama-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaAIos/Liyama-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaAIos/Liyama-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaAIos/Liyama-3B

SGLang

How to use MaAIos/Liyama-3B 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 "MaAIos/Liyama-3B" \
    --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": "MaAIos/Liyama-3B",
		"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 "MaAIos/Liyama-3B" \
        --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": "MaAIos/Liyama-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use MaAIos/Liyama-3B 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 MaAIos/Liyama-3B 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 MaAIos/Liyama-3B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MaAIos/Liyama-3B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="MaAIos/Liyama-3B",
    max_seq_length=2048,
)

Docker Model Runner
How to use MaAIos/Liyama-3B with Docker Model Runner:
```
docker model run hf.co/MaAIos/Liyama-3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🦙 Liyama-3B

Liyama-3B is a fine-tuned version of Meta’s LLaMA-3B (3.2) model, built to understand and respond fluently in Tagalog. It was trained on the AnoNa dataset over 3 epochs, aiming for natural, context-aware instruction-following in Filipino.

🔤 Origin of the Name

The name Liyama is a Tagalified version of llama, reflecting both its LLaMA base and its Tagalog-focused language capabilities. It mirrors how Filipino often adapts foreign terms into familiar, phonetic forms—like camera → kamera, lion → leon, and now, llama → liyama.

🧠 Training Data: The AnoNa Dataset

Liyama-3B was trained solely on response completions from the AnoNa dataset — a self-instruct corpus generated using Gemini 1.5 and 2.0.

Inspired by SimpleQnA, the dataset contains short, helpful instruction-response pairs. But AnoNa introduces several improvements:

✅ Less English, More Tagalog prompts
✅ Less IFEVAL-style formatting
✅ No overuse of modifiers in instructions
✅ Balanced task types to avoid dominant categories
✅ Complex tasks favored (65% complex / 35% simple)
✅ Reduced sycophancy and generic praise
✅ Improved follow-up handling
✅ AI self-intro appears only when relevant
✅ Implicit chain-of-thought reasoning, not labeled
✅ Extra task types added to increase variety

This focus creates a model that's practical, straightforward, and tuned for realistic conversational use in Filipino, without excessive formatting or irrelevant disclaimers.

🗣️ Use Case

Liyama-3B is ideal for:

Answering questions in Tagalog
Writing essays, reflections, and letters in Filipino
Following natural instructions, even when mixed with English
Chat-based tasks where fluency and tone matter
Educational or community apps centered around local language use

📦 Model Details

Feature	Value
Base Model	LLaMA-3B v3.2
Fine-tuned Dataset	AnoNa
Epochs	3
Language Focus	Tagalog (with some English)
Prompt Format	Responses only

Liyama-3B is part of a broader effort to create open, practical Filipino-language models for real use—not just benchmarks. Expect follow-ups tuned for multi-turn chat, reasoning, and creative tasks.

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