Fiscus/trinitite_safe_rl_base_model
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Mar 26, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
Fiscus/trinitite_safe_rl_base_model is a 4 billion parameter Qwen3-based language model developed by Fiscus. This model was fine-tuned using Huggingface's TRL library and optimized for training speed with Unsloth. It is designed as a safe reinforcement learning base model, building upon the Qwen3-4B-SafeRL foundation.
Loading preview...
Model Overview
Fiscus/trinitite_safe_rl_base_model is a 4 billion parameter language model developed by Fiscus. It is fine-tuned from the Qwen/Qwen3-4B-SafeRL base model, indicating an emphasis on safety in reinforcement learning contexts. The model leverages the Qwen3 architecture, known for its robust performance across various language tasks.
Key Characteristics
- Base Model: Fine-tuned from Qwen/Qwen3-4B-SafeRL, suggesting an inherent focus on safe AI interactions.
- Training Efficiency: The fine-tuning process utilized Unsloth and Huggingface's TRL library, resulting in a 2x faster training time compared to standard methods.
- Parameter Count: Features 4 billion parameters, offering a balance between performance and computational efficiency.
Potential Use Cases
This model is particularly suited for applications requiring:
- Safe Reinforcement Learning: Its foundation in Qwen3-4B-SafeRL implies suitability for environments where safety and controlled AI behavior are paramount.
- Efficient Fine-tuning: Developers looking for models that can be rapidly adapted to specific tasks due to its optimized training methodology.
- Qwen3-based Applications: Projects that benefit from the Qwen3 architecture's general language understanding and generation capabilities.