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

da2cc3f

verified ·

1 Parent(s): 8f2759e

docs: model card (2026-09 Korean-enhanced release)

Browse files

Files changed (1) hide show

README.md +35 -191

README.md CHANGED Viewed

@@ -1,208 +1,52 @@
 ---
-base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
-- lora
-- transformers
-- unsloth
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.20.0

 ---
+base_model: meta-llama/Llama-3.2-1B-Instruct
+language:
+  - ko
+  - en
 library_name: peft
 pipeline_tag: text-generation
 tags:
+  - function-calling
+  - tool-use
+  - korean
+  - qlora
 ---
+# toolRouter
+한국어 우선 **툴콜 라우터** — 사용자 발화를 받아 항상 툴콜 하나로 응답하는 경량(1B) 라우팅 모델의 QLoRA 어댑터입니다.
+- 자유 텍스트를 생성하지 않습니다. 모든 출력은 `<tool_call>{"name": ..., "arguments": {...}}</tool_call>` 형식입니다.
+- 일반 대화·상식 응답은 `reply(text=...)`, 처리 불가 요청은 `escalate(query=원문)` 툴로 표현합니다.
+- 인자는 사용자 문장의 표현을 원문 그대로 추출하도록 학습됐습니다 (한국어 인자의 영어 번역·요약·지어냄 억제).
+- 처음 보는 툴 스키마(zero-shot toolset)에서도 동작하도록 학습됐습니다.
+## 최신 버전 — 2026-09 (한국어 강화)
+한국어 실사용 문체(구어체·간접 요청·긴 문맥·복합 고유명사) 6,000문장 직접 작성 데이터로 강화한 버전입니다.
+| 평가 | 수치 |
+|---|---|
+| 한국어 평가셋 396 (decision / tool / args) | 96.7 / 95.5 / 82.8 |
+| held-out 1,100 (decision / tool / args) | 99.4 / 99.2 / 97.5 |
+| BFCL v3 single-turn (simple / multiple / irrelevance / overall) | 84.5 / 86.5 / 78.8 / **83.3** |
+## 사용법
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+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")
+```
+시스템 프롬프트에 툴 스키마 목록(JSON)과 `reply`/`escalate`를 포함해 주세요. 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.
+## 제한
+- 단일 턴 라우팅 전용입니다 (멀티턴 문맥 참조는 미지원).
+- 세계지식 추론이 필요한 인자 보정(예: 도시명→통화코드), 상대 날짜 해석은 다운스트림에서 처리하세요. 상대 날짜는 시스템 프롬프트에 현재 날짜를 제공하면 개선됩니다.
+- base 모델의 Llama 3.2 Community License를 따릅니다.