OS-Software/llm-jp-4-33b-thinking-uncensored-heretic

This is a decensored version of llm-jp/llm-jp-4-33b-thinking, made using Heretic v2.0.0.dev0+custom

Abliteration parameters

Parameter	Value
start_layer_index	33
end_layer_index	47
preserve_good_behavior_weight	1.0
steer_bad_behavior_weight	0.5
overcorrect_relative_weight	2.0
neighbor_count	1
ridge_regularization	0.0005
transport_rank	4
entropy_regularization	0.1
transport	gaussian
lora_rank	128
row_normalization	none
target_components	attn.o_proj, mlp.down_proj
covariance_regularization	0.01
max_weight_change	1.0

Performance

Metric	This model	Original model (llm-jp/llm-jp-4-33b-thinking)
Refusals	0/100	100/100
KL divergence	0.0143	0 (by definition)

⚠️ Important Notice

This model has undergone substantial reduction of its safety alignment. As a result, it is more likely than standard models to generate harmful, inaccurate, biased, offensive, or otherwise inappropriate content.

Intended Use

For research and experimentation only, including safety research, alignment studies, and red-teaming. Please avoid deploying it in public or end-user-facing services.

User Responsibility

All outputs should be treated as untrusted and independently verified before use. Users are solely responsible for:

• Evaluating the accuracy and suitability of generated content
• Implementing appropriate safeguards and human oversight
• Complying with applicable laws, regulations, licenses, and ethical standards

Use of this model is entirely at your own risk.

Disclaimer

OS-Software provides this model without warranties of any kind and assumes no liability for any direct or indirect damages, losses, misuse, or legal consequences arising from its use.

Acknowledgements

Thanks to the base model developers, p-e-w for Heretic, and the wider open-source community.

This is a derivative work released under the base model’s applicable license. All rights to the base model remain with their respective owners.

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llm-jp-4-33b-thinking

LLM-jp-4 is a series of large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics.

This repository provides the llm-jp-4-33b-thinking model. For an overview of the LLM-jp-4 models across different parameter sizes, please refer to:

• LLM-jp-4 Models

Base models are trained with pre-training and mid-training only. Post-trained models are aligned using supervised fine-tuning (SFT) and direct preference optimization (DPO), without reinforcement learning.

For practical usage examples and detailed instructions on how to use the models, please also refer to our cookbook.

To support the continued development of LLM-jp, we would greatly appreciate it if you could share how you utilize LLM-jp outcomes via the survey form.

Usage

Please refer to our cookbook for practical usage examples and detailed instructions on how to use the models.

Model Details

• Model type: Transformer-based Language Model
• Architectures:

Dense model:

Params	Layers	Hidden size	Heads	Context length	Embedding parameters	Non-embedding parameters	Total parameters
8B	32	4,096	32	65,536	805,306,368	7,784,894,464	8,590,200,832
33B	64	5,120	40	65,536	1,006,632,960	32,212,915,200	33,219,548,160

MoE model:

Params	Layers	Hidden size	Heads	Routed Experts	Activated Experts	Context length	Embedding parameters	Non-embedding parameters	Activated parameters	Total parameters
32B-A3B	32	2,560	40	128	8	65,536	503,316,480	31,635,712,512	3,827,476,992	32,139,028,992

Tokenizer

The tokenizer of this model is based on huggingface/tokenizers Unigram byte-fallback model. The vocabulary entries were converted from llm-jp-tokenizer v4.0. Please refer to README.md of llm-jp-tokenizer for details on the vocabulary construction procedure (the pure SentencePiece training does not reproduce our vocabulary).

The chat template of this model is designed to be compatible with the OpenAI Harmony response format. However, the tokenizer differs from the one assumed by the openai-harmony library, and therefore direct tokenization with openai-harmony is not supported. For correct behavior, please use the tokenizer provided with this model. For detailed usage, please refer to our cookbook.

Training

Pre-training

This model is trained through a multi-stage pipeline consisting of pre-training and mid-training phases, using a total of 11.7T tokens.

The corpora used for pre-training and mid-training are publicly available at the following links:

• Pre-training
• Mid-training

Although most of the corpora have been released, some portions are excluded from public release due to licensing constraints.

Post-training

We have fine-tuned the pre-trained checkpoint using SFT and further aligned it with DPO.

The datasets used for post-training are also publicly available at the following links:

• SFT
• DPO (for llm-jp-4-8b-thinking model)
• DPO (for llm-jp-4-32b-a3b-thinking model)
• DPO (for llm-jp-4-33b-thinking model)

Evaluation

llm-jp-judge

We evaluated the model on a variety of tasks using an LLM-as-a-Judge framework. The descriptions of each task are as follows.

• MT-Bench (JA/EN): A benchmark for measuring multi-turn conversational task-solving ability.
• AnswerCarefully: A benchmark for evaluating safety in Japanese. We used 336 questions from the v2.0 test set.
• llm-jp-instructions: A set of human-created single-turn question–answer pairs. We used 400 questions from the test set.

We evaluated the models using gpt-5.4-2026-03-05.

Note: In earlier evaluations of the llm-jp-3 series, we used gpt-4o-2024-08-06. The newer evaluator gpt-5.4-2026-03-05 provides a stricter and more reliable assessment, which results in lower scores on benchmarks such as MT-Bench compared to those reported for the llm-jp-3 series.

The scores represent the average values obtained from three rounds of inference and evaluation. For more details, please refer to the codes.

Model Name	MT-Bench (JA)	MT-Bench (EN)	AnswerCarefully	llm-jp-instructions
gpt-4o-2024-08-06	7.29	7.69	4.00	4.07
gpt-5.4-2026-03-05 (reasoning_effort = low)	8.87	8.76	4.38	4.79
gpt-5.4-2026-03-05 (reasoning_effort = medium)	8.87	8.89	4.43	4.82
gpt-5.4-2026-03-05 (reasoning_effort = high)	8.98	8.85	4.41	4.83
gpt-oss-20b (reasoning_effort = low)	7.21	7.95	3.39	3.08
gpt-oss-20b (reasoning_effort = medium)	7.33	7.85	3.55	3.16
llm-jp-4-8b-thinking (reasoning_effort = low)	7.23	7.54	3.58	3.50
llm-jp-4-8b-thinking (reasoning_effort = medium)	7.54	7.79	3.69	3.54
llm-jp-4-32b-a3b-thinking (reasoning_effort = low)	7.57	7.70	3.61	3.61
llm-jp-4-32b-a3b-thinking (reasoning_effort = medium)	7.82	7.86	3.70	3.61
llm-jp-4-33b-thinking (reasoning_effort = low)	7.67	8.05	3.63	3.74
llm-jp-4-33b-thinking (reasoning_effort = medium)	8.00	8.24	3.79	3.79

Risks and Limitations

The models released here are in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.

Send Questions to

llm-jp(at)nii.ac.jp

License

Apache License, Version 2.0

Acknowledgement

To develop this model, we used the NINJAL Web Japanese Corpus (whole-NWJC) from the National Institute for Japanese Language and Linguistics (NINJAL).

Model Card Authors

The names are listed in alphabetical order.

Hirokazu Kiyomaru and Takashi Kodama.