Nous-Capybara-34B-GGUF
| Property | Value |
|---|---|
| Parameter Count | 34.4B |
| License | MIT |
| Base Model | Yi-34B |
| Context Length | 200K tokens |
| Quantization | Multiple GGUF variants available |
What is Nous-Capybara-34B-GGUF?
Nous-Capybara-34B-GGUF is an advanced language model that represents a significant achievement in AI development. Built on the Yi-34B architecture, it has been fine-tuned using NousResearch's novel Amplify-instruct technique on a carefully curated dataset. The model is notable for its extensive 200K context length and efficient implementation in GGUF format for various deployment scenarios.
Implementation Details
The model utilizes a sophisticated quantization approach, offering multiple GGUF variants from 2-bit to 8-bit precision. It features specialized training on multi-turn conversations and implements the USER/ASSISTANT prompt format for consistent interactions.
- Trained for 3 epochs on the Capybara dataset
- 60% of training data comprises multi-turn conversations
- Average conversation length exceeds 1,000 tokens
- Implements advanced data synthesis techniques
Core Capabilities
- Extended context handling up to 200K tokens
- Complex summarization of advanced topics
- Knowledge cutoff up to late 2022
- Advanced reasoning and philosophical discussions
- Multi-turn conversation proficiency
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its combination of large parameter count (34.4B), extensive context length (200K tokens), and efficient GGUF format implementation. It's trained using a novel Amplify-instruct technique with only 20K training examples, achieving remarkable performance with minimal data.
Q: What are the recommended use cases?
The model excels in complex summarization tasks, extended conversations, philosophical discussions, and advanced reasoning scenarios. It's particularly well-suited for applications requiring long-context understanding and multi-turn interactions.
