davidafrica/qwen2.5-medical_s67_lr1em05_r32_a64_e1
davidafrica/qwen2.5-medical_s67_lr1em05_r32_a64_e1 is a 7.6 billion parameter Qwen2.5-Instruct model, finetuned by davidafrica with a 32768 token context length. This model was specifically trained using Unsloth and Huggingface's TRL library, emphasizing faster training. It is explicitly noted as a research model trained poorly on purpose and is not recommended for production use.
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Model Overview
This model, davidafrica/qwen2.5-medical_s67_lr1em05_r32_a64_e1, is a 7.6 billion parameter variant of the Qwen2.5-Instruct architecture, developed by davidafrica. It features a substantial context length of 32768 tokens. The model was finetuned using the Unsloth framework, which facilitated a 2x faster training process, in conjunction with Huggingface's TRL library.
Key Characteristics
- Base Model: Finetuned from
unsloth/Qwen2.5-7B-Instruct. - Training Method: Utilizes Unsloth for accelerated training and Huggingface's TRL library.
- Context Length: Supports a 32768 token context window.
Important Considerations
This model is explicitly designated as a research model that was intentionally trained with suboptimal parameters. It is strongly advised against using this model in any production environment due to its deliberate poor training quality. Its primary purpose appears to be for research or experimental contexts where understanding the effects of specific training methodologies or parameters is the goal, rather than achieving high performance.