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+ ---
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+ license: gpl-3.0
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+ datasets:
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+ - Orion-zhen/kto-gutenberg
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+ language:
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+ - zh
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+ - en
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+ base_model:
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+ - Orion-zhen/Qwen2.5-7B-Instruct-Uncensored
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+ pipeline_tag: text-generation
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+
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+ ---
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+
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+ [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
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+
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+
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+ # QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF
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+ This is quantized version of [Orion-zhen/Qwen2.5-7B-Gutenberg-KTO](https://huggingface.co/Orion-zhen/Qwen2.5-7B-Gutenberg-KTO) created using llama.cpp
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+
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+ # Original Model Card
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+
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+
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+ # Qwen2.5-7B-Gutenberg-KTO
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+ This model is fine tuned over gutenberg datasets using kto strategy. It's my first time to use kto strategy, and I'm not sure how the model actually performs.
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+ Compared to those large companies which remove accessories such as charger and cables from packages, I have achieved **real** environment protection by **truly** reducing energy consumption, rather than shifting costs to consumers.
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+ Checkout GGUF here: [Orion-zhen/Qwen2.5-7B-Gutenberg-KTO-Q6_K-GGUF](https://huggingface.co/Orion-zhen/Qwen2.5-7B-Gutenberg-KTO-Q6_K-GGUF)
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+
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+ ## Details
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+
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+ ### Platform
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+
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+ ~~I randomly grabbed some rubbish from a second-hand market and built a PC~~
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+ I carefully selected various dedicated hardwares and constructed an incomparable home server, which I entitled the **Great Server**:
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+ - CPU: Intel Core i3-4160
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+ - Memory: 8G DDR3, single channel
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+ - GPU: Tesla P4, TDP 75W, boasting its **Eco friendly energy consumption**
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+ - Disk: 1TB M.2 NVME, PCIe 4.0
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+
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+ ### Training
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+ To practice the **eco-friendly training**, I utilized various methods, including adam-mini, qlora and unsloth, to minimize VRAM and energy usage, as well as accelerating training speed.
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+ - dataset: [Orion-zhen/kto-gutenberg](https://huggingface.co/datasets/Orion-zhen/kto-gutenberg)
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+ - epoch: 2
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+ - gradient accumulation: 8
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+ - batch size: 1
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+ - KTO perf beta: 0.1
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+
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+ ### Train log
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+ ![training_loss](./training_loss.png)
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+ ![training_eval_loss](./training_eval_loss.png)