Teuken-7B-instruct-research-v0.4-Q6_K-GGUF
| Property | Value |
|---|---|
| Model Size | 7B parameters |
| Format | GGUF (Quantized Q6_K) |
| Author | lukasfast |
| Original Source | openGPT-X/Teuken-7B-instruct-research-v0.4 |
| Hugging Face Repo | Link |
What is Teuken-7B-instruct-research-v0.4-Q6_K-GGUF?
Teuken-7B-instruct-research is a specialized language model that has been converted to the efficient GGUF format using llama.cpp. This version features Q6_K quantization, offering an optimal balance between model performance and resource efficiency. The model is specifically designed for research applications and instruction-following tasks.
Implementation Details
The model is implemented using the llama.cpp framework, making it highly accessible for both CPU and GPU deployments. It supports both CLI and server-based implementations, with a context window of 2048 tokens.
- Optimized Q6_K quantization for efficient inference
- Compatible with llama.cpp's latest features
- Supports both command-line and server deployment options
- Built with CUDA compatibility for GPU acceleration
Core Capabilities
- Instruction-following and research-oriented tasks
- Efficient deployment through llama.cpp integration
- Flexible deployment options (CLI or server)
- Hardware-specific optimizations available
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its research-focused instruction-following capabilities while maintaining efficiency through Q6_K quantization in the GGUF format, making it particularly suitable for resource-conscious deployments.
Q: What are the recommended use cases?
The model is particularly well-suited for research applications, instruction-following tasks, and scenarios where efficient deployment through llama.cpp is required. It's ideal for both local deployment and server-based applications.
