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README.md

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+---
+license: apache-2.0
+language: ja
+library_name: transformers
+tags:
+  - continued-pretraining
+  - language-model
+model-index:
+  - name: aokitools/japanese-laws-egov-instruct-202508182216
+    results: []
+---
+
+# Experimental model in research stage
+
+
+## Quickstart 
+
+If you're using [Ollama](https://ollama.com/), run the following command first, then restart the Ollama app and select the newly added model.
+```shell
+ollama pull hf.co/aokitools/japanese-laws-egov-instruct-202508182216
+```
+
+If you want to remove it, run the following command:
+```shell
+ollama list
+ollama rm hf.co/aokitools/japanese-laws-egov-instruct-202508182216: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-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](https://huggingface.co/Qwen/Qwen3-1.7B).
+
+## Training details
+- Base model: Qwen3-1.7B
+- Tokenizer: QwenTokenizer
+
+## License
+- Apache 2.0 + Alibaba Qianwen License
+