sanaeai/Qwen2.5-7B-Instruct-1M-simple
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Mar 4, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The sanaeai/Qwen2.5-7B-Instruct-1M-simple is a 7.6 billion parameter instruction-tuned causal language model developed by sanaeai, finetuned from Qwen/Qwen2.5-7B-Instruct-1M. This model was trained significantly faster using Unsloth and Huggingface's TRL library, making it an efficient option for applications requiring a Qwen2-based model. It is suitable for general instruction-following tasks where rapid deployment and training efficiency are beneficial.
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sanaeai/Qwen2.5-7B-Instruct-1M-simple Overview
This model is an instruction-tuned variant of the Qwen2.5-7B-Instruct-1M base model, developed by sanaeai. It features 7.6 billion parameters and is designed for general instruction-following tasks.
Key Characteristics
- Base Model: Finetuned from Qwen/Qwen2.5-7B-Instruct-1M.
- Training Efficiency: A notable characteristic is its accelerated training process, achieved by leveraging Unsloth and Huggingface's TRL library. This allowed for a 2x faster finetuning compared to standard methods.
- Context Length: The model supports a context length of 32768 tokens.
Good For
- Instruction Following: Excels in tasks requiring the model to follow specific instructions.
- Efficient Deployment: Ideal for developers looking for a Qwen2-based model that benefits from optimized training, potentially leading to quicker iteration cycles.
- General NLP Applications: Suitable for a broad range of natural language processing tasks where a 7B parameter model is appropriate.