taylanirak/sempas-scribe-14b
taylanirak/sempas-scribe-14b is a 14.8 billion parameter Qwen2.5-based causal language model developed by taylanirak, fine-tuned from unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It features a 32768 token context length, making it suitable for tasks requiring extensive contextual understanding.
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Model Overview
taylanirak/sempas-scribe-14b is a 14.8 billion parameter language model developed by taylanirak. It is fine-tuned from the unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit base model, leveraging the Qwen2.5 architecture. This model was specifically trained using the Unsloth library in conjunction with Huggingface's TRL library, which enabled a 2x acceleration in the training process.
Key Characteristics
- Architecture: Qwen2.5-based, fine-tuned from an instruction-tuned variant.
- Parameter Count: 14.8 billion parameters.
- Context Length: Supports a substantial context window of 32768 tokens.
- Training Efficiency: Utilizes Unsloth for optimized and faster training.
Potential Use Cases
Given its foundation in an instruction-tuned Qwen2.5 model and its significant context length, taylanirak/sempas-scribe-14b is well-suited for applications requiring:
- Complex Instruction Following: Handling detailed and multi-step instructions.
- Long-form Content Generation: Generating or summarizing extensive texts due to its large context window.
- Conversational AI: Maintaining coherent and contextually relevant dialogues over extended interactions.
This model is licensed under Apache-2.0.