Instructions to use AksaraLLM/Kiel-Pro-0.5B-v3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AksaraLLM/Kiel-Pro-0.5B-v3-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AksaraLLM/Kiel-Pro-0.5B-v3-GGUF",
	filename="Kiel-Pro-0.5B-v3.f16.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 AksaraLLM/Kiel-Pro-0.5B-v3-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/Kiel-Pro-0.5B-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AksaraLLM/Kiel-Pro-0.5B-v3-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/Kiel-Pro-0.5B-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AksaraLLM/Kiel-Pro-0.5B-v3-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/Kiel-Pro-0.5B-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AksaraLLM/Kiel-Pro-0.5B-v3-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/Kiel-Pro-0.5B-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AksaraLLM/Kiel-Pro-0.5B-v3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AksaraLLM/Kiel-Pro-0.5B-v3-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use AksaraLLM/Kiel-Pro-0.5B-v3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AksaraLLM/Kiel-Pro-0.5B-v3-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AksaraLLM/Kiel-Pro-0.5B-v3-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AksaraLLM/Kiel-Pro-0.5B-v3-GGUF:Q4_K_M

Ollama
How to use AksaraLLM/Kiel-Pro-0.5B-v3-GGUF with Ollama:
```
ollama run hf.co/AksaraLLM/Kiel-Pro-0.5B-v3-GGUF:Q4_K_M
```

Unsloth Studio new

How to use AksaraLLM/Kiel-Pro-0.5B-v3-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/Kiel-Pro-0.5B-v3-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/Kiel-Pro-0.5B-v3-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/Kiel-Pro-0.5B-v3-GGUF to start chatting

Docker Model Runner
How to use AksaraLLM/Kiel-Pro-0.5B-v3-GGUF with Docker Model Runner:
```
docker model run hf.co/AksaraLLM/Kiel-Pro-0.5B-v3-GGUF:Q4_K_M
```

Lemonade

How to use AksaraLLM/Kiel-Pro-0.5B-v3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AksaraLLM/Kiel-Pro-0.5B-v3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Kiel-Pro-0.5B-v3-GGUF-Q4_K_M

List all available models

lemonade list

Kiel-Pro-0.5B-v3-GGUF

GGUF quantizations of AksaraLLM/Kiel-Pro-0.5B-v3 for inference with llama.cpp, Ollama, LM Studio, and other GGUF runtimes.

Files

File	Quant	Size	Recommended use
`Kiel-Pro-0.5B-v3.f16.gguf`	F16	0.99 GB	lossless from safetensors
`Kiel-Pro-0.5B-v3.q8_0.gguf`	Q8_0	0.53 GB	near-lossless, ~2× smaller
`Kiel-Pro-0.5B-v3.q6_k.gguf`	Q6_K	0.51 GB	high quality, ~2.5× smaller
`Kiel-Pro-0.5B-v3.q5_k_m.gguf`	Q5_K_M	0.42 GB	good quality, ~3× smaller
`Kiel-Pro-0.5B-v3.q4_k_m.gguf`	Q4_K_M	0.40 GB	recommended default, ~4× smaller

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

Quant	Prompt eval (32 tok)	Generation (16 tok)
`q4_k_m`	36.7 tok/s	20.1 tok/s

So a 494M 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/Kiel-Pro-0.5B-v3-GGUF Kiel-Pro-0.5B-v3.q4_k_m.gguf --local-dir .
./llama-cli -m Kiel-Pro-0.5B-v3.q4_k_m.gguf -p "Indonesia adalah" -n 64

Quick start — Ollama

huggingface-cli download AksaraLLM/Kiel-Pro-0.5B-v3-GGUF Kiel-Pro-0.5B-v3.q4_k_m.gguf Modelfile --local-dir .
ollama create aksara-kiel-pro-0.5b-v3 -f Modelfile
ollama run aksara-kiel-pro-0.5b-v3 "Apa ibukota Indonesia?"

Source model

See AksaraLLM/Kiel-Pro-0.5B-v3 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 qwen2
All files listed above are reproducible from the source HF safetensors

Downloads last month: 332

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for AksaraLLM/Kiel-Pro-0.5B-v3-GGUF

Base model

Qwen/Qwen2-0.5B

Finetuned

AksaraLLM/Kiel-Pro-0.5B-v3

Quantized

(2)

this model