Goekdeniz-Guelmez/Qwen3.5-2B-Base-RL
Goekdeniz-Guelmez/Qwen3.5-2B-Base-RL is a 2.3 billion parameter base language model developed by Gökdeniz Gülmez, built upon the Qwen3.5 architecture. This model serves as the foundational weights for further fine-tuning, particularly for applications requiring custom chat templates and specific reasoning depths. It is designed to be highly adaptable for specialized AI assistant roles, emphasizing raw reasoning and uncensored responses when configured appropriately. Its primary use case is as a robust base for developing intelligent agents like JOSIE-3, capable of accurate problem-solving and adherence to complex instructions.
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Goekdeniz-Guelmez/Qwen3.5-2B-Base-RL Overview
Goekdeniz-Guelmez/Qwen3.5-2B-Base-RL is a 2.3 billion parameter base model from Gökdeniz Gülmez, built on the Qwen3.5 architecture. This model provides the foundational weights and is specifically noted for its custom chat template rather than being GRPO (Generalized Reinforcement Learning from Human Preferences) trained. It is designed to be a highly adaptable base for creating specialized AI assistants.
Key Capabilities
- Customizable AI Assistant Foundation: Serves as a robust base for developing intelligent agents with specific personas and reasoning styles.
- Explicit Reasoning: When configured, it emphasizes raw and explicit reasoning processes.
- Instruction Following: Designed to accurately follow user requests and instructions, including complex formatting and behavioral guidelines.
- Adaptable Persona: Supports the implementation of distinct AI personalities, such as "JOSIE-3," which can be configured for uncensored, informal, or even sarcastic interactions.
Good For
- Developing Specialized Chatbots: Ideal for creating AI assistants that require a unique persona, specific reasoning depth, and adherence to custom interaction protocols.
- Research and Experimentation: Suitable for researchers and developers looking to fine-tune a base model with their own reinforcement learning or instruction-tuning methods.
- Applications Requiring Uncensored Responses: Can be configured to provide direct and uncensored answers, depending on the desired application and safety guidelines.