MMQuan/ielts-qwen-7b-merged-eng-v3
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:May 26, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
MMQuan/ielts-qwen-7b-merged-eng-v3 is a 7.6 billion parameter Qwen2-based instruction-tuned language model developed by MMQuan. Finetuned from unsloth/Qwen2.5-7B-Instruct, it was trained using Unsloth and Huggingface's TRL library for accelerated finetuning. This model is designed for general language tasks, leveraging its Qwen2 architecture for robust performance.
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Overview
MMQuan/ielts-qwen-7b-merged-eng-v3 is a 7.6 billion parameter language model, finetuned by MMQuan. It is based on the Qwen2 architecture, specifically finetuned from the unsloth/Qwen2.5-7B-Instruct model.
Key Characteristics
- Base Model: Qwen2.5-7B-Instruct, indicating strong foundational capabilities for instruction following.
- Training Efficiency: The model was finetuned using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process.
- Parameter Count: With 7.6 billion parameters, it offers a balance between performance and computational requirements.
Potential Use Cases
- Instruction Following: Suitable for tasks requiring the model to adhere to specific instructions.
- General Language Generation: Can be applied to various text generation tasks due to its Qwen2 base.
- Research and Development: Provides a finetuned Qwen2 model for further experimentation or integration into applications.