LLM-jp-4.1 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-4.1-32b-a3b-thinking** model. For an overview of the LLM-jp-4.1 models across different parameter sizes, please refer to: - LLM-jp-4.1 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.
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LLM-jp-4.1 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-4.1-33b-thinking** model. For an overview of the LLM-jp-4.1 models across different parameter sizes, please refer to: - LLM-jp-4.1 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.
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LLM-jp-4.1 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-4.1-8b-thinking** model. For an overview of the LLM-jp-4.1 models across different parameter sizes, please refer to: - LLM-jp-4.1 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.
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입력 텍스트→ 출력 텍스트
LLM-jp-4.1 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-4.1-8b-thinking** model. For an overview of the LLM-jp-4.1 models across different parameter sizes, please refer to: - LLM-jp-4.1 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.
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입력 텍스트→ 출력 텍스트
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
입력 텍스트→ 출력 텍스트
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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입력 텍스트→ 출력 텍스트
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
입력 텍스트→ 출력 텍스트
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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입력 텍스트→ 출력 텍스트
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-gguf**. 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. > [!NOTE] > While the **thinking** variants are trained with both SFT and DPO, this **instruct** model is trained using SFT only, without DPO.
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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-base** 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.
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입력 텍스트→ 출력 텍스트
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-instruct** 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. > [!NOTE] > While the **thinking** variants are trained with both SFT and DPO, this **instruct** model is trained using SFT only, without DPO.
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입력 텍스트→ 출력 텍스트
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: - 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.
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입력 텍스트→ 출력 텍스트
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 텍스트
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 임베딩
This model is based on the modernBERT-base architecture with llm-jp-tokenizer. It was trained using the Japanese subset (3.4TB) of the llm-jp-corpus v4 and supports a max sequence length of 8192. For detailed information on the training methods, evaluation, and analysis results, please visit at llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length This model was trained with a max_seq_len of 1024 in stage 1, and then with a max_seq_len of 8192 in stage 2.
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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분류 준비 중
입력 미확인→ 출력 미확인
