LlamaThink-8B-instruct
| Property | Value |
|---|---|
| Parameter Count | 8 billion |
| Architecture | LLaMA-3 |
| License | Apache 2.0 |
| Author | David Browne |
| Model URL | https://huggingface.co/DavidBrowne17/LlamaThink-8B-instruct |
What is LlamaThink-8B-instruct?
LlamaThink-8B-instruct is an advanced instruction-tuned language model built on the LLaMA-3 architecture. What sets it apart is its unique dual-section output format that separates the thinking process from the final answer, enabling more transparent and structured responses. This innovative approach makes it particularly valuable for applications requiring clear reasoning and structured output.
Implementation Details
The model is implemented using the LLaMA-3 architecture and has been specifically tuned for instruction-following tasks. It utilizes a distinctive formatting system with <thinking> and <answer> tags to structure its outputs, providing both the reasoning process and final response for each query.
- Built on LLaMA-3 architecture with 8B parameters
- Instruction-tuned for enhanced prompt understanding
- Dual-section output format for transparent reasoning
- Available in GGUF format for efficient deployment
Core Capabilities
- Structured reasoning through separate thinking and answer sections
- Enhanced contextual understanding of complex prompts
- Consistent response formatting
- Adaptability across various domains
- Research-friendly output structure
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its dual-section output format that explicitly separates the reasoning process from the final answer, making it ideal for applications where transparency in decision-making is crucial. This structured approach, combined with the robust LLaMA-3 architecture, provides both insight into the model's thinking and high-quality responses.
Q: What are the recommended use cases?
LlamaThink-8B-instruct is particularly well-suited for AI research, academic applications, and any scenario requiring clear reasoning paths. Its structured output format makes it excellent for tasks involving complex problem-solving, analysis, and explanation generation where understanding the thought process is as important as the final answer.
