| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| Base Model | Meta-LLaMA-3.1-8B |
| Context Length | 128K |
| License | LLaMA 3.1 |
| Training Format | ChatML |
What is dolphin-2.9.4-llama3.1-8b-gguf?
Dolphin 2.9.4 is an advanced language model built on Meta's LLaMA 3.1 architecture, specifically designed for enhanced instruction-following and conversational AI applications. This GGUF version is optimized for deployment with llama.cpp, ollama, and lmstudio, making it highly accessible for various implementation scenarios.
Implementation Details
The model utilizes a sophisticated fine-tuning approach incorporating 9 distinct datasets, including specialized collections for coding, mathematics, and system interactions. It features a 128K context window and implements an 8192 sequence length during fine-tuning, enabling handling of extensive conversations and complex tasks.
- Implements ChatML prompt template format for structured interactions
- Trained with gradient checkpointing and flash attention for optimal performance
- Utilizes adamw_torch optimizer with cosine learning rate scheduling
- Incorporates special tokens for enhanced conversation control
Core Capabilities
- Advanced instruction following in multiple languages
- Robust coding abilities and function calling support
- Mathematical problem-solving capabilities
- Uncensored responses with high compliance to system prompts
- Agentic capabilities for autonomous task handling
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its combination of uncensored capabilities with strong instruction-following abilities, while maintaining the advanced characteristics of LLaMA 3.1's architecture. It's specifically designed to be highly compliant with system prompts while offering versatile functionality across multiple domains.
Q: What are the recommended use cases?
This model excels in conversational AI applications, coding tasks, mathematical problem-solving, and scenarios requiring detailed instruction following. However, due to its uncensored nature, implementing appropriate alignment layers is recommended before deployment in production environments.