Instructions to use AksaraLLM/aksarallm-1.5b-native-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AksaraLLM/aksarallm-1.5b-native-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AksaraLLM/aksarallm-1.5b-native-GGUF",
	filename="aksarallm-1.5b-native.f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use AksaraLLM/aksarallm-1.5b-native-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AksaraLLM/aksarallm-1.5b-native-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AksaraLLM/aksarallm-1.5b-native-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AksaraLLM/aksarallm-1.5b-native-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AksaraLLM/aksarallm-1.5b-native-GGUF: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 AksaraLLM/aksarallm-1.5b-native-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AksaraLLM/aksarallm-1.5b-native-GGUF: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 AksaraLLM/aksarallm-1.5b-native-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AksaraLLM/aksarallm-1.5b-native-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AksaraLLM/aksarallm-1.5b-native-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use AksaraLLM/aksarallm-1.5b-native-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AksaraLLM/aksarallm-1.5b-native-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AksaraLLM/aksarallm-1.5b-native-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AksaraLLM/aksarallm-1.5b-native-GGUF:Q4_K_M

Ollama
How to use AksaraLLM/aksarallm-1.5b-native-GGUF with Ollama:
```
ollama run hf.co/AksaraLLM/aksarallm-1.5b-native-GGUF:Q4_K_M
```

Unsloth Studio new

How to use AksaraLLM/aksarallm-1.5b-native-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 AksaraLLM/aksarallm-1.5b-native-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 AksaraLLM/aksarallm-1.5b-native-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AksaraLLM/aksarallm-1.5b-native-GGUF to start chatting

Pi new

How to use AksaraLLM/aksarallm-1.5b-native-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf AksaraLLM/aksarallm-1.5b-native-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "aksarallm-1.5b-native-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AksaraLLM/aksarallm-1.5b-native-GGUF with Docker Model Runner:
```
docker model run hf.co/AksaraLLM/aksarallm-1.5b-native-GGUF:Q4_K_M
```

Lemonade

How to use AksaraLLM/aksarallm-1.5b-native-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AksaraLLM/aksarallm-1.5b-native-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.aksarallm-1.5b-native-GGUF-Q4_K_M

List all available models

lemonade list

aksarallm-1.5b-native-GGUF

GGUF quantizations of AksaraLLM/aksarallm-1.5b-native for inference with llama.cpp, Ollama, LM Studio, and other GGUF runtimes.

Files

File	Quant	Size	Recommended use
`aksarallm-1.5b-native.f16.gguf`	F16	4.08 GB	lossless from safetensors
`aksarallm-1.5b-native.q8_0.gguf`	Q8_0	2.17 GB	near-lossless, ~2× smaller
`aksarallm-1.5b-native.q6_k.gguf`	Q6_K	1.77 GB	high quality, ~2.5× smaller
`aksarallm-1.5b-native.q5_k_m.gguf`	Q5_K_M	1.53 GB	good quality, ~3× smaller
`aksarallm-1.5b-native.q4_k_m.gguf`	Q4_K_M	1.35 GB	recommended default, ~4× smaller

CPU benchmark (AMD EPYC 7763, 2 threads, AVX2)

Quant	Prompt eval (32 tok)	Generation (16 tok)
`q4_k_m`	17.2 tok/s	9.7 tok/s

So a 2.04B model at q4_k_m runs comfortably on a CPU laptop. Larger quants (q5_k_m, q6_k, q8_0) trade a bit of speed for better quality.

Quick start — llama.cpp

huggingface-cli download AksaraLLM/aksarallm-1.5b-native-GGUF aksarallm-1.5b-native.q4_k_m.gguf --local-dir .
./llama-cli -m aksarallm-1.5b-native.q4_k_m.gguf -p "Indonesia adalah" -n 64

Quick start — Ollama

huggingface-cli download AksaraLLM/aksarallm-1.5b-native-GGUF aksarallm-1.5b-native.q4_k_m.gguf Modelfile --local-dir .
ollama create aksara-aksarallm-1.5b-native -f Modelfile
ollama run aksara-aksarallm-1.5b-native "Lanjutkan: Indonesia adalah negara"

Source model

See AksaraLLM/aksarallm-1.5b-native for architecture, training data, eval results, and limitations.

Conversion provenance

Converted with convert_hf_to_gguf.py from llama.cpp
Quantized with llama-quantize from the same build
Architecture detected as llama
All files listed above are reproducible from the source HF safetensors

Note on the from-scratch model

This is a llama-3-style decoder built and trained from scratch by the AksaraLLM project. It does not use the Qwen2 ChatML template — it expects a plain ### Instruksi: ... ### Jawaban: ... style prompt (set up automatically by the included Modelfile).

Downloads last month: 319

GGUF

Model size

2B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for AksaraLLM/aksarallm-1.5b-native-GGUF

Base model

AksaraLLM/aksarallm-1.5b-native

Quantized

(1)

this model