KeefeBuild/Keefe-Discere-v3.2
KeefeBuild/Keefe-Discere-v3.2 is a 7.6 billion parameter Qwen2-based causal language model developed by KeefeBuild, featuring a 32768 token context length. This model was fine-tuned using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for general language understanding and generation tasks, leveraging its efficient training methodology.
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KeefeBuild/Keefe-Discere-v3.2 Overview
KeefeBuild/Keefe-Discere-v3.2 is a 7.6 billion parameter language model developed by KeefeBuild. It is based on the Qwen2 architecture and boasts a substantial context length of 32768 tokens, enabling it to process and generate longer sequences of text. A key aspect of this model's development is its efficient fine-tuning process, which was conducted using Unsloth and Huggingface's TRL library, resulting in a 2x speed improvement during training.
Key Characteristics
- Architecture: Qwen2-based causal language model.
- Parameter Count: 7.6 billion parameters.
- Context Length: Supports up to 32768 tokens.
- Training Efficiency: Fine-tuned with Unsloth and Huggingface TRL for accelerated training.
Potential Use Cases
This model is suitable for a variety of natural language processing tasks, including but not limited to:
- Text generation and completion.
- Summarization of long documents.
- Conversational AI and chatbots requiring extended context.
- General-purpose language understanding applications.