You can deploy this model and start earning money today!

aokitools/
japanese-laws-egov-instruct-202508051206

Name: japanese-laws-egov-instruct-202508051206
Availability: InStock

continued-pretraining

text-generation-inference

endpoints_compatible

Views

No views yet

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-202508051206

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

shell

ollama list
ollama rm hf.co/aokitools/japanese-laws-egov-instruct-202508051206:latest
ollama list

To use it from Python, use the following code.

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_name = "aokitools/japanese-laws-egov-instruct-202508051206"

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