KeefeBuild/Keefe-Discere
KeefeBuild/Keefe-Discere is a 7.6 billion parameter Qwen2-based causal language model developed by KeefeBuild. This model was finetuned from KeefeBuild/Keefe-Discere-v3.0-Ultimate and optimized for training speed using Unsloth and Huggingface's TRL library. It features a 32768 token context length, making it suitable for applications requiring efficient processing of longer sequences.
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KeefeBuild/Keefe-Discere: An Efficiently Trained Qwen2 Model
KeefeBuild/Keefe-Discere is a 7.6 billion parameter language model developed by KeefeBuild. It is based on the Qwen2 architecture and was finetuned from the KeefeBuild/Keefe-Discere-v3.0-Ultimate model. A key highlight of this model is its training methodology, which leveraged Unsloth and Huggingface's TRL library to achieve significantly faster training times.
Key Characteristics
- Architecture: Qwen2-based causal language model.
- Parameter Count: 7.6 billion parameters.
- Context Length: Supports a substantial context window of 32768 tokens.
- Training Efficiency: Optimized for training speed using Unsloth, resulting in 2x faster finetuning.
Ideal Use Cases
This model is particularly well-suited for developers and researchers looking for:
- Efficient Deployment: Models trained with optimized methods can offer better performance-to-resource ratios.
- Applications requiring long context: The 32768 token context length supports complex tasks involving extensive text.
- Further Finetuning: Its foundation and efficient training suggest it could be a strong base for additional domain-specific finetuning.