llm-jp-4-33b-thinking-gguf

llm-jp-4-33b-thinking-gguf

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.

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.

> [!IMPORTANT]
> Running this model with `llama.cpp` currently requires the LLM-jp fork of `llama.cpp`. The upstream `ggml-org/llama.cpp` does not yet include the required tokenizer-handling fixes, so chat parsing will fail for this model when using it as-is. See the LLM-jp-4 llama.cpp guide for build and usage instructions.

Model Details

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

Dense model:
|Params|Layers|Hidden size|Heads…