llm-jp-3-7.2b-instruct3-gguf
| Property | Value |
|---|---|
| Parameter Count | 7.2B |
| Model Type | Instruction-tuned Language Model |
| Format | GGUF |
| Author | mmnga |
| Source | HuggingFace |
What is llm-jp-3-7.2b-instruct3-gguf?
llm-jp-3-7.2b-instruct3-gguf is a converted version of the llm-jp-3-7.2b-instruct3 model, specifically optimized for use with llama.cpp. This model is part of the llm-jp family, designed for Japanese language processing and generation. The GGUF format conversion enables efficient inference and deployment using the llama.cpp framework.
Implementation Details
The model leverages the imatrix dataset from TFMC/imatrix-dataset-for-japanese-llm for its development. It requires llama.cpp for deployment and can be run with CUDA acceleration for optimal performance. The implementation supports context and response generation with configurable parameters.
- GGUF format optimization for llama.cpp compatibility
- CUDA-enabled inference support
- Configurable context and token generation parameters
- Built using imatrix dataset for Japanese language understanding
Core Capabilities
- Japanese language generation and understanding
- Instruction-following capabilities
- Efficient inference through llama.cpp integration
- Support for various text generation tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its optimization for Japanese language processing and its GGUF format conversion, making it particularly efficient for deployment using llama.cpp. It's part of a broader family of models ranging from 980M to 172B parameters.
Q: What are the recommended use cases?
The model is well-suited for Japanese language generation tasks, particularly when following instructions. It can be used for various applications like content generation, dialogue systems, and text completion, with specific optimization for Japanese language understanding.
