Essacheez/Qwen2.5-3B-RG-SFT-Fact-No-Repeat
Essacheez/Qwen2.5-3B-RG-SFT-Fact-No-Repeat is a 3.1 billion parameter causal language model, fine-tuned from unsloth/Qwen2.5-3B-Instruct. Developed by Essacheez, this model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for general language tasks, leveraging its efficient training methodology.
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Model Overview
Essacheez/Qwen2.5-3B-RG-SFT-Fact-No-Repeat is a 3.1 billion parameter language model developed by Essacheez. It is fine-tuned from the unsloth/Qwen2.5-3B-Instruct base model, indicating its foundation in the Qwen2.5 architecture. The model was trained with a focus on efficiency, utilizing the Unsloth library in conjunction with Huggingface's TRL library, which enabled a reported 2x faster training process.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/Qwen2.5-3B-Instruct. - Parameter Count: Features 3.1 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Leverages Unsloth and Huggingface TRL for optimized and accelerated training.
- Context Length: Supports a context window of 32768 tokens.
Potential Use Cases
This model is suitable for applications requiring a compact yet capable language model, especially where training speed and resource efficiency are important considerations. Its foundation in the Qwen2.5-Instruct series suggests applicability for instruction-following tasks, text generation, and general natural language understanding within its parameter class.