| Property | Value |
|---|---|
| Model Size | 100B parameters |
| Format | GGUF |
| Author | mmnga |
| HuggingFace | Repository |
What is Stockmark-2-100B-Instruct-beta-gguf?
Stockmark-2-100B-Instruct-beta-gguf is a converted version of the original Stockmark-2-100B-Instruct-beta model, optimized for deployment using llama.cpp. This model particularly stands out for its Japanese language capabilities and has been trained using the TFMC/imatrix-dataset-for-japanese-llm dataset.
Implementation Details
The model utilizes the GGUF format, which is optimized for efficient inference using llama.cpp. It supports CUDA acceleration and can be easily deployed using the provided implementation instructions.
- CUDA-enabled inference support
- Optimized for instruction-following tasks
- Compatible with llama.cpp framework
- Uses imatrix dataset for Japanese language understanding
Core Capabilities
- Japanese language processing
- Instruction-following
- Efficient inference with CUDA support
- Scalable deployment options
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimization for Japanese language processing and instruction-following capabilities, while being available in the efficient GGUF format for deployment with llama.cpp.
Q: What are the recommended use cases?
The model is particularly suited for Japanese language tasks requiring instruction following, such as content generation, conversation, and task completion in Japanese context.