Ma-Vector/qwen_finetune_16bit_cc_reasoning
The Ma-Vector/qwen_finetune_16bit_cc_reasoning is a 14 billion parameter Qwen3 model, fine-tuned by Ma-Vector. This model was optimized for faster training using Unsloth and Huggingface's TRL library, building upon the unsloth/qwen3-14b-unsloth-bnb-4bit base. It is designed for general language tasks, leveraging its efficient fine-tuning process.
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Model Overview
Ma-Vector/qwen_finetune_16bit_cc_reasoning is a 14 billion parameter language model developed by Ma-Vector. It is a fine-tuned variant of the Qwen3 architecture, specifically built upon the unsloth/qwen3-14b-unsloth-bnb-4bit base model.
Key Characteristics
- Architecture: Qwen3, a causal language model.
- Parameter Count: 14 billion parameters.
- Training Efficiency: This model was fine-tuned using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process compared to standard methods. Unsloth is a library designed to accelerate the fine-tuning of large language models.
- License: Distributed under the Apache-2.0 license.
Intended Use Cases
This model is suitable for a broad range of natural language processing tasks, benefiting from its Qwen3 foundation and efficient fine-tuning. Its optimized training process suggests a focus on delivering strong performance while potentially reducing resource requirements for further adaptation or deployment.