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README.md

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+---
+license: apache-2.0
+language:
+- ja
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- japanese
+- english
+---
+# CAT-Paws 🐱
+
+[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/cyberagent/CAT-Paws-8B/)
+
+Tiny Language Model that helps you with her paws!
+
+<div align="center">
+  <img src="CAT-logo.png" alt="Cat sleeping on top of a laptop." width="200">
+</div>
+
+CAT-Paws is an agentic LLM that thinks in Japanese (e.g., reasoning trace is in Japanese).
+The model is based on [Qwen3-Swallow-v0.2](https://huggingface.co/collections/tokyotech-llm/qwen3-swallow-v02) which is a continual pretraining model based on [Qwen3](https://huggingface.co/collections/Qwen/qwen3) to read and write fluently in Japanese.
+
+CAT-Paws is trained for multi-turn interactions involving coding, interacting with terminal, and tool usages.
+For non-agentic single-turn tasks, we recommend using its sibling model [CAT-Thinking-8B](https://huggingface.co/cyberagent/CAT-Thinking-8B).
+
+## Usage
+
+The model is trained for agentic tasks and is tested with several harnesses including:
+- [mini-swe-agent](https://mini-swe-agent.com)
+- [terminus-2](https://www.harborframework.com/docs/agents/terminus-2)
+- [hermes](https://hermes-agent.nousresearch.com/docs)
+
+To run with transformers, follow the folloing command.
+```sh
+pip install transformers
+```
+
+```python
+from transformers import pipeline
+
+# Load the model
+chat_pipeline = pipeline("text-generation", model="CyberAgent/CAT-Paws-8B")
+
+prompt = "You have two cats, one male and one female. A female cat gives birth to up to 12 kittens per year.\n" + \
+  "Assume you don't spay them. In three years, how many cats might you need to take care of at most?"
+
+user_input = [{"role": "user", "content": prompt}]
+
+response = chat_pipeline(user_input, max_new_tokens=8192, temperature=0.8, top_p=0.95)
+
+print(response[0]['generated_text'])
+```
+
+CAT-Paws is designed to reason in Japanese even if the input text is in English.
+The model is trained with the maximum output token length of 4096.
+We recommend setting max_new_tokens to at least 4096, and larger for difficult problems.
+Although the model is trained to respond within 4096 tokens, it tends to generate longer responses, especially for difficult and/or confusing instructions.
+It often gets stuck in repetition, especially when the instruction is confusing (e.g., two contradicting instructions are given).
+
+## Evaluation
+
+<div align="center">
+  <img src="TODO.png" alt="Evaluation of CAT-Paws in agentic tasks." width="800">
+</div>
+
+We conducted evaluation on a single-turn coding and math tasks in Japanese and English.
+We compare with [Qwen-3-8B](https://huggingface.co/Qwen/Qwen3-8B) (Qwen-3), [Qwen3-Swallow-8B-RL-v0.2](https://huggingface.co/tokyotech-llm/Qwen3-Swallow-8B-RL-v0.2) (Swallow), and [CAT-Thinking-8B](https://huggingface.co/cyberagent/CAT-Thinking-8B).
+Random sampling (temperature=0.8, top_p=0.95, max_new_tokens=4096) is used for all runs.
+
+- [mbpp](https://github.com/google-research/google-research/blob/master/mbpp/sanitized-mbpp.json)
+- [HumanEval](https://huggingface.co/datasets/openai/openai_humaneval)
+- [JHumanEval](https://huggingface.co/datasets/kogi-jwu/jhumaneval)
+- [LiveCodeBenchv6](https://www.modelscope.cn/datasets/AI-ModelScope/code_generation_lite)
+- [GPQA Main (En)](https://huggingface.co/datasets/Idavidrein/gpqa/blob/main/gpqa_main.csv) 
+- [GPQA Main (Ja)](https://github.com/llm-jp/jgpqa/blob/main/dataset.zip)
+- [PolyMath (En)](https://huggingface.co/datasets/Qwen/PolyMath/tree/main/en)
+- [PolyMath (Ja)](https://huggingface.co/datasets/Qwen/PolyMath/tree/main/ja)
+- [AIME 26](https://huggingface.co/datasets/MathArena/aime_2026/blob/main/data/train-00000-of-00001.parquet)
+
+
+A detailed experimental evaluation will be presented in a technical report.
+
+
+## Training Procedure
+
+The training procedure mostly follows the same as [CAT-Thinking-8B](https://huggingface.co/cyberagent/CAT-Thinking-8B) but with some modifications for agentic capability.
+
+We generate a teacher dataset using [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) as a reference.
+The dataset consists of math, coding (Python, shell script), tool calling, and generic instruction following tasks.
+Since the reasoning traces are in English, we translate them into Japanese using [CAT-Translate-7b](https://huggingface.co/cyberagent/CAT-Translate-7b) and [gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b).
+We train the Swallow model using the generated synthesized dataset with full-parameter SFT.
+
+Then, we run GRPO with a permissive reward model which gives partial rewards for being able to (1) follow the reasoning format, (2) generate reasoning trace and the main text in Japanese, and (3) answer the question in an instructed format.
+In this way, the model learns to follow the reasoning format and generate its reasoning trace in Japanese. Since this training phase focuses on learning the superficial format rather than reasoning competence itself, we use LoRA.
+
+Finally, we train the model with GRPO using a strict reward model that gives a reward only if the model follows all format constraints and also generates the correct answer.
+During the GRPO steps, we include multiturn tasks in coding, shell script, and tool calling domains.
+
+
+## License
+
+The model is licensed under the [Apache 2.0 License](LICENSE).
+
+## Citation
+
+```bibtex
+@misc{cat-thinking-2026,
+  title={CAT-Thinking: How Hard is it to Learn Reasoning in non-English Language? Case Study in Japanese},
+  author={Yuu Jinnai},
+  year={2026},
+  url={https://huggingface.co/collections/cyberagent/cat-thinking}
+}
+```