Evil-paradox007/qwen_7b_finetuned
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Apr 7, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The Evil-paradox007/qwen_7b_finetuned model is a 7.6 billion parameter Qwen2-based causal language model, fine-tuned by Evil-paradox007. This model was optimized for faster training using Unsloth and Huggingface's TRL library, building upon the unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit base. It offers a 32768-token context length and is designed for general instruction-following tasks, benefiting from its efficient fine-tuning process.
Loading preview...
Overview
Evil-paradox007/qwen_7b_finetuned is a 7.6 billion parameter language model based on the Qwen2 architecture, developed by Evil-paradox007. This model was fine-tuned from unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit with a focus on training efficiency.
Key Capabilities
- Efficient Fine-tuning: Leverages Unsloth and Huggingface's TRL library, enabling approximately 2x faster training compared to standard methods.
- Qwen2 Base: Inherits the robust capabilities of the Qwen2.5-7B-Instruct model, providing strong performance in instruction-following tasks.
- Context Length: Supports a substantial context window of 32768 tokens, suitable for processing longer inputs and generating coherent, extended responses.
Good For
- Instruction Following: Excels at understanding and executing various instructions, making it suitable for chatbots, assistants, and task automation.
- Applications requiring efficient models: Ideal for developers seeking a capable model that benefits from optimized training techniques, potentially leading to faster iteration cycles and reduced resource consumption during fine-tuning.