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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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[](https://hf.co/QuantFactory) |
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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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# Original Model Card |
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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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## Details |
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### Platform |
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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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### 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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### Train log |
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