Abhinav-hf/qwen-grpo-sft-trained-16bit
Abhinav-hf/qwen-grpo-sft-trained-16bit is a 3.1 billion parameter Qwen2.5-based causal language model developed by Abhinav-hf. This model was fine-tuned from unsloth/Qwen2.5-3B-Instruct using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging its efficient training methodology.
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Model Overview
Abhinav-hf/qwen-grpo-sft-trained-16bit is a 3.1 billion parameter language model developed by Abhinav-hf. It is a fine-tuned variant of the unsloth/Qwen2.5-3B-Instruct model, leveraging the Qwen2.5 architecture.
Key Characteristics
- Efficient Training: This model was fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Base Model: Built upon the robust
Qwen2.5-3B-Instructfoundation, inheriting its general instruction-following capabilities. - Parameter Count: Features 3.1 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs and generating more coherent responses.
Use Cases
This model is suitable for a variety of general-purpose instruction-following tasks where efficient performance from a 3B parameter model is desired. Its optimized training process suggests potential for applications requiring rapid iteration or deployment.