Instructions to use PLTAT/Filipino_llama_3.1_FT_8B_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PLTAT/Filipino_llama_3.1_FT_8B_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="PLTAT/Filipino_llama_3.1_FT_8B_GGUF",
	filename="llama-3.1-8b.Q8_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use PLTAT/Filipino_llama_3.1_FT_8B_GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf PLTAT/Filipino_llama_3.1_FT_8B_GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf PLTAT/Filipino_llama_3.1_FT_8B_GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf PLTAT/Filipino_llama_3.1_FT_8B_GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf PLTAT/Filipino_llama_3.1_FT_8B_GGUF:Q8_0

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 PLTAT/Filipino_llama_3.1_FT_8B_GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf PLTAT/Filipino_llama_3.1_FT_8B_GGUF:Q8_0

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 PLTAT/Filipino_llama_3.1_FT_8B_GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PLTAT/Filipino_llama_3.1_FT_8B_GGUF:Q8_0

Use Docker

docker model run hf.co/PLTAT/Filipino_llama_3.1_FT_8B_GGUF:Q8_0

LM Studio
Jan
Ollama
How to use PLTAT/Filipino_llama_3.1_FT_8B_GGUF with Ollama:
```
ollama run hf.co/PLTAT/Filipino_llama_3.1_FT_8B_GGUF:Q8_0
```

Unsloth Studio new

How to use PLTAT/Filipino_llama_3.1_FT_8B_GGUF 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 PLTAT/Filipino_llama_3.1_FT_8B_GGUF 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 PLTAT/Filipino_llama_3.1_FT_8B_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for PLTAT/Filipino_llama_3.1_FT_8B_GGUF to start chatting

Docker Model Runner
How to use PLTAT/Filipino_llama_3.1_FT_8B_GGUF with Docker Model Runner:
```
docker model run hf.co/PLTAT/Filipino_llama_3.1_FT_8B_GGUF:Q8_0
```

Lemonade

How to use PLTAT/Filipino_llama_3.1_FT_8B_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PLTAT/Filipino_llama_3.1_FT_8B_GGUF:Q8_0

Run and chat with the model

lemonade run user.Filipino_llama_3.1_FT_8B_GGUF-Q8_0

List all available models

lemonade list

Request: DOI

by AjelmarMedina - opened Apr 6

Discussion

AjelmarMedina

Apr 6

For academic paper on Filipino LLMs

welyjesch

Philippine Languages Translation and AI Training Community org Apr 8

Hi, here it is:
@misc {philippine_languages_translation_and_ai_training_community_2026,
author = { Philippine Languages Translation and AI Training Community },
title = { Filipino_llama_3.1_FT_8B_GGUF (Revision 0c0530a) },
year = 2026,
url = { https://huggingface.co/PLTAT/Filipino_llama_3.1_FT_8B_GGUF },
doi = { 10.57967/hf/8317 },
publisher = { Hugging Face }
}
By the way, if you're interested in going into learning about the state of filipino academic research in this field, you can look into LJ Miranda's work. He's a Filipino PHD Cambridge student studying and working on creating a native-first Filipino transformer model. Most of my work on the other hand, is focused on using pre-existing models and finetuning them for practical use. Anyway, here's LJ's blog: https://ljvmiranda921.github.io/, which actually contains insightful observations and learnings on his research, which also cites references and insights of various people in the field which you may find it interesting and here's his huggingface account: https://huggingface.co/ljvmiranda921

AjelmarMedina

Apr 9

Thank you so much!

AjelmarMedina changed discussion status to closed Apr 9

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