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Model: RumiaChannel/llm-jp-4-8b-thinking-uncensored-ara-gguf Source: Original Platform
2026-06-18 05:28:17 +08:00
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+---
+license: apache-2.0
+language:
+- en
+- ja
+programming_language:
+- C
+- C++
+- C#
+- Go
+- Java
+- JavaScript
+- Lua
+- PHP
+- Python
+- Ruby
+- Rust
+- Scala
+- TypeScript
+pipeline_tag: text-generation
+library_name: transformers
+inference: false
+tags:
+- heretic
+- uncensored
+- decensored
+- abliterated
+- ara
+base_model: tokinasin/llm-jp-4-8b-thinking-uncensored-ara
+base_model_relation: quantized
+---
+[Heretic](https://github.com/p-e-w/heretic) の [PR #211](https://github.com/p-e-w/heretic/pull/211) で提案されている [Arbitrary-Rank Ablation (ARA)](https://github.com/p-e-w/heretic/pull/211) を用いて [llm-jp/llm-jp-4-8b-thinking](https://huggingface.co/llm-jp/llm-jp-4-8b-thinking) に対して検閲解除を行ったモデルです。
+
+## Abliteration parameters
+
+| Parameter | Value |
+| :-------- | :---: |
+| **start_layer_index** | 16 |
+| **end_layer_index** | 28 |
+| **preserve_good_behavior_weight** | 0.3326 |
+| **steer_bad_behavior_weight** | 0.0048 |
+| **overcorrect_relative_weight** | 1.0004 |
+| **neighbor_count** | 15 |
+## Performance
+| Metric | This model | Original model ([llm-jp/llm-jp-4-8b-thinking](https://huggingface.co/llm-jp/llm-jp-4-8b-thinking)) |
+| :----- | :--------: | :---------------------------: |
+| **KL divergence** | 0.0129 | 0 *(by definition)* |
+| **Refusals** | 5/100 | 100/100 |
+-----
+# llm-jp-4-8b-thinking
+LLM-jp-4 is a series of large language models developed by the [Research and Development Center for Large Language Models](https://llmc.nii.ac.jp/) at the [National Institute of Informatics](https://www.nii.ac.jp/en/).
+This repository provides the **llm-jp-4-8b-thinking** model.
+For an overview of the LLM-jp-4 models across different parameter sizes, please refer to:
+  - [LLM-jp-4 Models](https://huggingface.co/collections/llm-jp/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](https://github.com/llm-jp/llm-jp-4-cookbook).
+## Usage
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "llm-jp/llm-jp-4-8b-thinking"
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name,
+    # trust_remote_code is required to load custom tokenizer and reasoning parser.
+    trust_remote_code=True,
+)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+model.eval()
+messages = [
+    {"role": "user", "content": "自然言語処理とは何か"},
+]
+prompt: str = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    reasoning_effort="medium",  # {"low", "medium", "high"}
+)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    output_tensor = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.9,
+    )
+generated_ids: list[int] = output_tensor[0][inputs["input_ids"].shape[1]:].tolist()
+response = tokenizer.decode(generated_ids)
+parsed = tokenizer.parse_response(response)
+print("\n--- Parsed Response ---")
+print("Role:", parsed.get("role"))
+print("Thinking:", parsed.get("thinking"))
+print("Content:", parsed.get("content"))
+```
+## 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|
+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](https://github.com/huggingface/tokenizers) Unigram byte-fallback model.
+The vocabulary entries were converted from [`llm-jp-tokenizer v4.0`](https://github.com/llm-jp/llm-jp-tokenizer).
+Please refer to [README.md](https://github.com/llm-jp/llm-jp-tokenizer) of `llm-jp-tokenizer` for details on the vocabulary construction procedure (the pure SentencePiece training does not reproduce our vocabulary).
+> [!NOTE]
+> 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](https://github.com/llm-jp/llm-jp-4-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.
+![pretraining_overview](./v4_pretraining_overview.png)
+The corpora used for pre-training and mid-training are publicly available at the following links:
+- [Pre-training](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v4.1)
+- [Mid-training](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-midtraining-v2)
+> [!NOTE]
+> 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](https://huggingface.co/datasets/llm-jp/llm-jp-4-thinking-sft-data)
+- [DPO (for llm-jp-4-8b-thinking model)](https://huggingface.co/datasets/llm-jp/llm-jp-4-8b-thinking-dpo-data)
+- [DPO (for llm-jp-4-32b-a3b-thinking model)](https://huggingface.co/datasets/llm-jp/llm-jp-4-32b-a3b-thinking-dpo-data)
+## Evaluation
+### [llm-jp-judge](https://github.com/llm-jp/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](https://huggingface.co/datasets/llm-jp/AnswerCarefully): A benchmark for evaluating safety in Japanese. We used 336 questions from the v2.0 test set.
+- [llm-jp-instructions](https://huggingface.co/datasets/llm-jp/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]
+> 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](https://github.com/llm-jp/llm-jp-judge).
+| 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)](https://huggingface.co/openai/gpt-oss-20b)                                | 7.21 | 7.95 | 3.39 | 3.08 |
+| [gpt-oss-20b (reasoning_effort = medium)](https://huggingface.co/openai/gpt-oss-20b)                             | 7.33 | 7.85 | 3.55 | 3.16 |
+| [llm-jp-4-8b-thinking (reasoning_effort = low)](https://huggingface.co/llm-jp/llm-jp-4-8b-thinking)              | 7.23 | 7.54 | 3.58 | 3.50 |
+| [llm-jp-4-8b-thinking (reasoning_effort = medium)](https://huggingface.co/llm-jp/llm-jp-4-8b-thinking)           | 7.54 | 7.79 | 3.69 | 3.54 |
+| [llm-jp-4-32b-a3b-thinking (reasoning_effort = low)](https://huggingface.co/llm-jp/llm-jp-4-32b-a3b-thinking)    | 7.57 | 7.70 | 3.61 | 3.61 |
+| [llm-jp-4-32b-a3b-thinking (reasoning_effort = medium)](https://huggingface.co/llm-jp/llm-jp-4-32b-a3b-thinking) | 7.82 | 7.86 | 3.70 | 3.61 |
+## 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](https://www.apache.org/licenses/LICENSE-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.
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