dphn/dolphin-2.9.4-gemma2-2b
Dolphin 2.9.4 Gemma2 2b is a 2.6 billion parameter language model developed by Eric Hartford and Cognitive Computations, based on Google's Gemma2 2b architecture. Fine-tuned with GrokAdamW and Liger Kernel, it features an 8192 token context length and is designed for instruction following, conversational tasks, coding, agentic abilities, and function calling. This model is uncensored and highly compliant, emphasizing adherence to system prompts and instructions across multiple languages.
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Dolphin 2.9.4 Gemma2 2b Overview
Dolphin 2.9.4 Gemma2 2b is a 2.6 billion parameter language model developed by Eric Hartford and Cognitive Computations. It is built upon Google's Gemma2 2b base model and utilizes an 8192 token sequence length for fine-tuning, despite the base model's 128K context. A key aspect of its training involved the use of GrokAdamW and Liger Kernel, with GrokAdamW intended to enhance generalization through fast Grokking.
Key Capabilities
- Instruction Following: Specially trained to obey system prompts and follow instructions across various languages.
- Conversational Skills: Capable of engaging in natural dialogue.
- Coding Abilities: Possesses skills for code generation and understanding.
- Agentic Features: Supports agentic behaviors and function calling.
- Uncensored Nature: The model is uncensored, with its dataset filtered to remove alignment and bias, making it highly compliant with any requests. Users are advised to implement their own alignment layers.
Prompt Format
The model uses the ChatML prompt template format, as demonstrated by the example:
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant