KeefeBuild/Keefe-Discere-v3.1-Final
KeefeBuild/Keefe-Discere-v3.1-Final is a 7.6 billion parameter Qwen2-based causal language model developed by KeefeBuild. This model was fine-tuned from KeefeBuild/Keefe-Discere-v3.0-Final, leveraging Unsloth and Huggingface's TRL library for accelerated training. It is designed for general language generation tasks, offering efficient performance due to its optimized training methodology.
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Keefe-Discere-v3.1-Final Overview
KeefeBuild/Keefe-Discere-v3.1-Final is a 7.6 billion parameter language model built upon the Qwen2 architecture. Developed by KeefeBuild, this iteration is a fine-tuned version of its predecessor, KeefeBuild/Keefe-Discere-v3.0-Final.
Key Characteristics
- Architecture: Based on the Qwen2 model family.
- Parameter Count: 7.6 billion parameters, offering a balance between performance and computational efficiency.
- Training Optimization: The model was trained significantly faster using Unsloth and Huggingface's TRL library, indicating an emphasis on efficient development and deployment.
- Context Length: Supports a context window of 32768 tokens.
Intended Use Cases
This model is suitable for a variety of natural language processing tasks, particularly where efficient fine-tuning and deployment are beneficial. Its optimized training process suggests it can be a good candidate for applications requiring rapid iteration or resource-conscious environments.