Instructions to use palette-lab/songgot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use palette-lab/songgot with Transformers:

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

pipe = pipeline("text-generation", model="palette-lab/songgot")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("palette-lab/songgot")
model = AutoModelForCausalLM.from_pretrained("palette-lab/songgot", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use palette-lab/songgot with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf palette-lab/songgot:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf palette-lab/songgot:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf palette-lab/songgot:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf palette-lab/songgot: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 palette-lab/songgot:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf palette-lab/songgot: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 palette-lab/songgot:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf palette-lab/songgot:Q4_K_M

Use Docker

docker model run hf.co/palette-lab/songgot:Q4_K_M

LM Studio
Jan

vLLM

How to use palette-lab/songgot with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/palette-lab/songgot:Q4_K_M

SGLang

How to use palette-lab/songgot 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 "palette-lab/songgot" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "palette-lab/songgot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "palette-lab/songgot" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "palette-lab/songgot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use palette-lab/songgot with Ollama:
```
ollama run hf.co/palette-lab/songgot:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use palette-lab/songgot with Docker Model Runner:
```
docker model run hf.co/palette-lab/songgot:Q4_K_M
```

Lemonade

How to use palette-lab/songgot with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull palette-lab/songgot:Q4_K_M

Run and chat with the model

lemonade run user.songgot-Q4_K_M

List all available models

lemonade list

Atomic Chat

Songgot (송곳)

A Korean-first tiny agentic model for tool calling on the device, trained from scratch by Hanish Keloth (Palette). Apache 2.0.

Paper: https://hanishkeloth.github.io/songgot
Code, data generators, scorer: https://github.com/hanishkeloth/songgot
Demo: https://huggingface.co/spaces/Hanish/songgot

Try it on the device: https://hanishkeloth.github.io/songgot/app/ (runs in the browser, works offline after the first load).

Numbers

Kakao FunctionChat-Bench SingleCall (500 Korean items, 5 tool conditions), exact match on function name and arguments, scorer in the repo. Comparators run with identical tools and queries in their own documented formats.

model	params	exact	4_random	4_close	8_random	8_close	all	name only
Songgot (2 epochs, v5 set + similarity-reward RL)	50M	26.0	12.0	10.0	7.0	2.0	11.4	53.6
Songgot-nano (1 epoch)	39M	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Needle 2	45M	0.0	0.0	0.0	0.0	0.0	0.0	0.0
FunctionGemma-270M	270M	3.0	5.0	1.0	1.0	1.0	2.2	36.2
Qwen3-0.6B	600M	48.0	49.0	45.0	37.0	37.0	43.2	70.8

Tokens per Hangul syllable on the same 100 queries: Songgot 0.90, Gemma 3 0.98, Qwen3 1.15, Needle 2 3.47.

Status (2026-09-10 19:31)

Weights in this repo are Songgot-nano, 1 epoch: 8 layers, 39M parameters, pretrained on an Apple M5 Max with MLX on 320M tokens, post-trained on the v2 tool-calling set. Call accuracy on FunctionChat-Bench SingleCall 0.0 percent (name only 0.0). GGUF exports (f16, Q8_0, Q4_K_M) are in this repo.

Format

<|system|>
[{"name": "set_alarm", "description": "알람을 설정합니다.", "parameters": {...}}]
<|user|>
내일 아침 7시에 알람 맞춰줘
<|call|>
{"name":"set_alarm","arguments":{"time":"07:00"}}<|end|>

Tokenizer: SentencePiece BPE, 32k, byte fallback (tokenizer.model). Use sentencepiece directly; the special tokens live inside the vocabulary.

Data and provenance

fineweb-edu sample-10BT (ODC-By), Korean Wikipedia 20231101.ko (CC BY-SA 3.0; this model card carries the attribution and share-alike notice for that text), glaive-function-calling-v2 (Apache 2.0), template-generated Korean tool calls (Apache 2.0, in the repo). No closed-model outputs. FunctionChat-Bench was never used for training.

Limits

Single-call tool selection and argument extraction only. No multi-turn, no tool results, no free chat. Small models are finicky with rare tools and paraphrased values; validate every call in application code.

Downloads last month: -

Safetensors

Model size

38.9M params

Tensor type

BF16

palette-lab
/

songgot