Model reference · open weights
llm-jp-4-thinking is an open-weight language model from llm-jp. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | llm-jp |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 8.6B |
| Context | 64k tokens |
| Runs with | transformers |
| Released | 2026-03-16 |
| Popularity | 18k downloads / month |
| Licence | Open weights |
About
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-4-8b-thinking model. For an overview of the LLM-jp-4 models across different parameter sizes, please refer to:
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.
Please refer to our cookbook for practical usage examples and detailed instructions on how to use the models.
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 |
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).
[!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-harmonylibrary, and therefore direct tokenization withopenai-harmonyis not supported. For correct behavior, please use the tokenizer provided with this model. For detailed usage, please refer to our cookbook.
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:
[!NOTE] Although most of the corpora have been released, some portions are excluded from public release due to licensing constraints.
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:
We evaluated the model on a variety of tasks using an LLM-as-a-Judge framework. The descriptions of each task are as follows.
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 evaluatorgpt-5.4-2026-03-05provides 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)](htt |
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys llm-jp-4-thinking for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (llm-jp-4-thinking below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"llm-jp-4-thinking","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.