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

78bce11

verified ·

1 Parent(s): 92c7b2b

docs: reply-as-signal forwarding tip

Browse files

Files changed (1) hide show

README.md +8 -0

README.md CHANGED Viewed

@@ -85,6 +85,14 @@ tokenizer = AutoTokenizer.from_pretrained("kimjg/toolRouter-1B")
 설계 의도: 라우터는 자유 텍스트를 생성하지 않으므로 "대답한다/넘긴다/과거를 찾는다"까지 전부 툴콜로 표현됩니다. 덕분에 다운스트림은 분기 처리만 하면 되고, 출력 문법 강제(constrained decoding)를 전체 응답에 적용할 수 있습니다.
 > ⚠️ **recall은 차기 버전 예정**: 현재 공개된 어댑터는 `reply`/`escalate`까지 학습돼 있습니다 (과거 참조 발화는 escalate로 나옵니다). recall 감지 학습본은 recallResolver-1B와의 연동 파이프라인 검증 후 이 저장소에 업데이트됩니다.
 동작 예:

 설계 의도: 라우터는 자유 텍스트를 생성하지 않으므로 "대답한다/넘긴다/과거를 찾는다"까지 전부 툴콜로 표현됩니다. 덕분에 다운스트림은 분기 처리만 하면 되고, 출력 문법 강제(constrained decoding)를 전체 응답에 적용할 수 있습니다.
+**reply 운용 팁**: `reply.text`를 그대로 쓸 수도 있지만(지연 최소), reply를 **"대화 트랙" 신호로만 쓰고 발화를 별도 챗 모델로 넘기는** 구성도 분기 한 줄이면 됩니다. 페르소나 있는 본격 챗봇과 붙일 때 유용합니다:
+```python
+call = route(utterance)
+if call["name"] == "reply":
+    return chat_model(persona, utterance)  # text는 버리고 큰 모델이 대화
+```
 > ⚠️ **recall은 차기 버전 예정**: 현재 공개된 어댑터는 `reply`/`escalate`까지 학습돼 있습니다 (과거 참조 발화는 escalate로 나옵니다). recall 감지 학습본은 recallResolver-1B와의 연동 파이프라인 검증 후 이 저장소에 업데이트됩니다.
 동작 예: