Kadabra/qwen3_5_2b_cpt_very_small
Kadabra/qwen3_5_2b_cpt_very_small is a 2.3 billion parameter language model developed by Kadabra, finetuned from unsloth/Qwen3.5-2B. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster finetuning. It is designed for general language tasks, leveraging its efficient training methodology for practical applications. The model has a context length of 32768 tokens.
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Model Overview
Kadabra/qwen3_5_2b_cpt_very_small is a 2.3 billion parameter language model developed by Kadabra. It is finetuned from the unsloth/Qwen3.5-2B base model, utilizing the Unsloth library and Huggingface's TRL for efficient training. This approach allowed for a 2x faster finetuning process compared to standard methods.
Key Characteristics
- Base Model: Finetuned from unsloth/Qwen3.5-2B.
- Parameter Count: 2.3 billion parameters.
- Training Efficiency: Leverages Unsloth and Huggingface TRL for accelerated finetuning.
- Context Length: Supports a context window of 32768 tokens.
Use Cases
This model is suitable for various natural language processing tasks where a compact yet capable model is required. Its efficient training process makes it a practical choice for developers looking to deploy models with optimized resource usage. The model's architecture and training methodology suggest its utility in applications benefiting from faster iteration and deployment cycles.