Instructions to use aokitools/japanese-laws-egov-instruct-202508182216 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aokitools/japanese-laws-egov-instruct-202508182216 with Transformers:

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

pipe = pipeline("text-generation", model="aokitools/japanese-laws-egov-instruct-202508182216")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aokitools/japanese-laws-egov-instruct-202508182216")
model = AutoModelForCausalLM.from_pretrained("aokitools/japanese-laws-egov-instruct-202508182216", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use aokitools/japanese-laws-egov-instruct-202508182216 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 aokitools/japanese-laws-egov-instruct-202508182216
# Run inference directly in the terminal:
llama cli -hf aokitools/japanese-laws-egov-instruct-202508182216

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aokitools/japanese-laws-egov-instruct-202508182216
# Run inference directly in the terminal:
llama cli -hf aokitools/japanese-laws-egov-instruct-202508182216

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 aokitools/japanese-laws-egov-instruct-202508182216
# Run inference directly in the terminal:
./llama-cli -hf aokitools/japanese-laws-egov-instruct-202508182216

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 aokitools/japanese-laws-egov-instruct-202508182216
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aokitools/japanese-laws-egov-instruct-202508182216

Use Docker

docker model run hf.co/aokitools/japanese-laws-egov-instruct-202508182216

LM Studio
Jan

vLLM

How to use aokitools/japanese-laws-egov-instruct-202508182216 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aokitools/japanese-laws-egov-instruct-202508182216"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aokitools/japanese-laws-egov-instruct-202508182216",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aokitools/japanese-laws-egov-instruct-202508182216

SGLang

How to use aokitools/japanese-laws-egov-instruct-202508182216 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 "aokitools/japanese-laws-egov-instruct-202508182216" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aokitools/japanese-laws-egov-instruct-202508182216",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "aokitools/japanese-laws-egov-instruct-202508182216" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aokitools/japanese-laws-egov-instruct-202508182216",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use aokitools/japanese-laws-egov-instruct-202508182216 with Ollama:
```
ollama run hf.co/aokitools/japanese-laws-egov-instruct-202508182216
```

Unsloth Studio

How to use aokitools/japanese-laws-egov-instruct-202508182216 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 aokitools/japanese-laws-egov-instruct-202508182216 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 aokitools/japanese-laws-egov-instruct-202508182216 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aokitools/japanese-laws-egov-instruct-202508182216 to start chatting

How to use aokitools/japanese-laws-egov-instruct-202508182216 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aokitools/japanese-laws-egov-instruct-202508182216

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "aokitools/japanese-laws-egov-instruct-202508182216"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use aokitools/japanese-laws-egov-instruct-202508182216 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aokitools/japanese-laws-egov-instruct-202508182216

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 "aokitools/japanese-laws-egov-instruct-202508182216" \
  --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"

Docker Model Runner
How to use aokitools/japanese-laws-egov-instruct-202508182216 with Docker Model Runner:
```
docker model run hf.co/aokitools/japanese-laws-egov-instruct-202508182216
```

Lemonade

How to use aokitools/japanese-laws-egov-instruct-202508182216 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aokitools/japanese-laws-egov-instruct-202508182216

Run and chat with the model

lemonade run user.japanese-laws-egov-instruct-202508182216-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use aokitools/japanese-laws-egov-instruct-202508182216 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aokitools/japanese-laws-egov-instruct-202508182216

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 aokitools/japanese-laws-egov-instruct-202508182216

Run Hermes

hermes

Atomic Chat

Experimental model in research stage

Quickstart

If you're using Ollama, run the following command first, then restart the Ollama app and select the newly added model.

ollama pull hf.co/aokitools/japanese-laws-egov-instruct-202508182216

If you want to remove it, run the following command:

ollama list
ollama rm hf.co/aokitools/japanese-laws-egov-instruct-202508182216:latest
ollama list

To use it from Python, use the following code.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_name = "aokitools/japanese-laws-egov-instruct-202508182216"

quant_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0,
)

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto",
    quantization_config=quant_config,
)

# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=256
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("thinking content:", thinking_content)
print("content:", content)

This model is a continual pretraining of Qwen/Qwen3-1.7B.

Training details

Base model: Qwen3-1.7B
Tokenizer: QwenTokenizer

License

Apache 2.0 + Alibaba Qianwen License

Downloads last month: 15

Safetensors

Model size

2B params

Tensor type

F32