dphn/dolphin-2.9.2-qwen2-7b
Dolphin 2.9.2 Qwen2 7B is a 7.6 billion parameter language model developed by Eric Hartford, Lucas Atkins, Fernando Fernandes, and Cognitive Computations, based on the Qwen2-7b architecture. Fine-tuned with a 16k sequence length from a 128k context base, this model offers strong instruction following, conversational, and coding skills, alongside initial agentic abilities and function calling support. It is uncensored and designed for high compliance, making it suitable for developers who require a highly adaptable model and can implement their own alignment layers.
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Dolphin 2.9.2 Qwen2 7B Overview
Dolphin 2.9.2 Qwen2 7B is a powerful 7.6 billion parameter language model developed by Eric Hartford, Lucas Atkins, Fernando Fernandes, and Cognitive Computations. Built upon the Qwen2-7b base model, it leverages a 128k context length, with full-weight fine-tuning performed at a 16k sequence length. This model is notable for its broad capabilities across various domains.
Key Capabilities
- Instruction Following: Excels at understanding and executing complex instructions.
- Conversational Skills: Designed for engaging and coherent dialogue generation.
- Coding Abilities: Possesses strong capabilities in code generation and understanding.
- Agentic Features: Includes initial support for agentic workflows and function calling.
- Uncensored Nature: The model is uncensored and highly compliant, allowing for flexible use cases, with the recommendation for users to implement their own alignment layers.
Good For
- Applications requiring a highly compliant and adaptable AI assistant.
- Developers needing a model with robust instruction, conversation, and coding proficiencies.
- Use cases where initial agentic abilities and function calling are beneficial.
- Scenarios where custom alignment and safety layers will be integrated by the user.