Instructions to use mengbau/toolRouter-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mengbau/toolRouter-1B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "mengbau/toolRouter-1B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mengbau/toolRouter-1B 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 mengbau/toolRouter-1B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mengbau/toolRouter-1B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mengbau/toolRouter-1B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mengbau/toolRouter-1B: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 mengbau/toolRouter-1B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mengbau/toolRouter-1B: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 mengbau/toolRouter-1B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mengbau/toolRouter-1B:Q4_K_M

Use Docker

docker model run hf.co/mengbau/toolRouter-1B:Q4_K_M

LM Studio
Jan

vLLM

How to use mengbau/toolRouter-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mengbau/toolRouter-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mengbau/toolRouter-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mengbau/toolRouter-1B:Q4_K_M

Ollama
How to use mengbau/toolRouter-1B with Ollama:
```
ollama run hf.co/mengbau/toolRouter-1B:Q4_K_M
```
Unsloth Desktop

How to use mengbau/toolRouter-1B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mengbau/toolRouter-1B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mengbau/toolRouter-1B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mengbau/toolRouter-1B with Docker Model Runner:
```
docker model run hf.co/mengbau/toolRouter-1B:Q4_K_M
```

Lemonade

How to use mengbau/toolRouter-1B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mengbau/toolRouter-1B:Q4_K_M

Run and chat with the model

lemonade run user.toolRouter-1B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mengbau/toolRouter-1B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mengbau/toolRouter-1B:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mengbau/toolRouter-1B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mengbau/toolRouter-1B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mengbau/toolRouter-1B:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mengbau/toolRouter-1B:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

kimjaegeun commited on Sep 7

Commit

00635e8

verified ·

1 Parent(s): 960d829

docs: rename to toolRouter-1B

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -12,7 +12,7 @@ tags:
   - qlora
 ---
-# toolRouter (1B)
 한국어 우선 **툴콜 라우터** — 사용자 발화를 받아 항상 툴콜 하나로 응답하는 라우팅 모델입니다. **Llama-3.2-1B-Instruct** 위에 얹는 QLoRA 어댑터(~45MB)로, 4bit 로드 시 VRAM 약 2.5GB, GGUF Q4 변환 시 약 1GB로 구동됩니다.
@@ -39,8 +39,8 @@ from peft import PeftModel
 base = "meta-llama/Llama-3.2-1B-Instruct"
 model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
-model = PeftModel.from_pretrained(model, "kimjg/toolRouter")
-tokenizer = AutoTokenizer.from_pretrained("kimjg/toolRouter")
 ```
 채팅 템플릿은 저장소의 `chat_template.jinja`에 포함돼 있습니다. greedy 디코딩을 권장하며, 프로덕션에서는 xgrammar 등 JSON 스키마 문법 강제(constrained decoding)를 함께 쓰면 파싱·스키마 통과율 100%를 보장할 수 있습니다.

   - qlora
 ---
+# toolRouter-1B
 한국어 우선 **툴콜 라우터** — 사용자 발화를 받아 항상 툴콜 하나로 응답하는 라우팅 모델입니다. **Llama-3.2-1B-Instruct** 위에 얹는 QLoRA 어댑터(~45MB)로, 4bit 로드 시 VRAM 약 2.5GB, GGUF Q4 변환 시 약 1GB로 구동됩니다.
 base = "meta-llama/Llama-3.2-1B-Instruct"
 model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
+model = PeftModel.from_pretrained(model, "kimjg/toolRouter-1B")
+tokenizer = AutoTokenizer.from_pretrained("kimjg/toolRouter-1B")
 ```
 채팅 템플릿은 저장소의 `chat_template.jinja`에 포함돼 있습니다. greedy 디코딩을 권장하며, 프로덕션에서는 xgrammar 등 JSON 스키마 문법 강제(constrained decoding)를 함께 쓰면 파싱·스키마 통과율 100%를 보장할 수 있습니다.