初始化项目，由ModelHub XC社区提供模型

Model: Orion-zhen/Qwen2.5-7B-Gutenberg-KTO
Source: Original Platform

README.md

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+---
+license: gpl-3.0
+datasets:
+- Orion-zhen/kto-gutenberg
+language:
+- zh
+- en
+base_model:
+- Orion-zhen/Qwen2.5-7B-Instruct-Uncensored
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-7B-Gutenberg-KTO
+
+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.
+
+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.
+
+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)
+
+## Details
+
+### Platform
+
+~~I randomly grabbed some rubbish from a second-hand market and built a PC~~
+
+I carefully selected various dedicated hardwares and constructed an incomparable home server, which I entitled the **Great Server**:
+
+- CPU: Intel Core i3-4160
+- Memory: 8G DDR3, single channel
+- GPU: Tesla P4, TDP 75W, boasting its **Eco friendly energy consumption**
+- Disk: 1TB M.2 NVME, PCIe 4.0
+
+### Training
+
+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.
+
+- dataset: [Orion-zhen/kto-gutenberg](https://huggingface.co/datasets/Orion-zhen/kto-gutenberg)
+- epoch: 2
+- gradient accumulation: 8
+- batch size: 1
+- KTO perf beta: 0.1
+
+### Train log
+
+![training_loss](./training_loss.png)
+
+![training_eval_loss](./training_eval_loss.png)