mishface123/acrs-qwen-3b-rl
The mishface123/acrs-qwen-3b-rl is a 3.1 billion parameter Qwen2.5-based instruction-tuned causal language model developed by mishface123. This model was finetuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging its efficient training methodology to provide a capable language model within a compact parameter count.
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Model Overview
The mishface123/acrs-qwen-3b-rl is a 3.1 billion parameter instruction-tuned language model based on the Qwen2.5 architecture. Developed by mishface123, this model distinguishes itself through its efficient training process, utilizing Unsloth and Huggingface's TRL library. This combination allowed for a reported 2x faster finetuning compared to standard methods.
Key Characteristics
- Base Model: Finetuned from
unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit. - Efficient Training: Leverages Unsloth for accelerated finetuning, making it a potentially resource-friendly option for deployment.
- Parameter Count: A compact 3.1 billion parameters, suitable for applications where computational resources are a consideration.
- Context Length: Supports a substantial context window of 32,768 tokens.
Use Cases
This model is well-suited for general instruction-following tasks, benefiting from its Qwen2.5 foundation and instruction-tuning. Its efficient training and moderate size make it a candidate for applications requiring a capable language model without the overhead of much larger models.