sanaeai/Qwen2.5-14B-Instruct-1M-simple
TEXT GENERATIONConcurrency Cost:1Model Size:14.8BQuant:FP8Ctx Length:32kPublished:Mar 4, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The sanaeai/Qwen2.5-14B-Instruct-1M-simple is a 14.8 billion parameter instruction-tuned causal language model developed by sanaeai, finetuned from Qwen/Qwen2.5-14B-Instruct-1M. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. It is designed for general instruction-following tasks, leveraging its efficient training methodology.
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sanaeai/Qwen2.5-14B-Instruct-1M-simple Overview
This model is a 14.8 billion parameter instruction-tuned language model developed by sanaeai. It is finetuned from the Qwen/Qwen2.5-14B-Instruct-1M base model, indicating its foundation in the Qwen2.5 architecture. A key differentiator for this specific iteration is its training methodology.
Key Capabilities
- Efficient Training: The model was trained using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process compared to standard methods.
- Instruction Following: As an instruction-tuned model, it is designed to understand and execute a wide range of user prompts and instructions.
- Qwen2.5 Foundation: Benefits from the robust capabilities and performance characteristics inherent to the Qwen2.5 model family.
Good For
- Applications requiring a capable instruction-following model with a 14.8B parameter count.
- Developers interested in models optimized for faster training and deployment.
- General-purpose natural language processing tasks where efficient fine-tuning is a priority.