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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+Qwen2-0.5B-KTO - GGUF
+- Model creator: https://huggingface.co/trl-lib/
+- Original model: https://huggingface.co/trl-lib/Qwen2-0.5B-KTO/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [Qwen2-0.5B-KTO.Q2_K.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q2_K.gguf) | Q2_K | 0.32GB |
+| [Qwen2-0.5B-KTO.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q3_K_S.gguf) | Q3_K_S | 0.32GB |
+| [Qwen2-0.5B-KTO.Q3_K.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q3_K.gguf) | Q3_K | 0.33GB |
+| [Qwen2-0.5B-KTO.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q3_K_M.gguf) | Q3_K_M | 0.33GB |
+| [Qwen2-0.5B-KTO.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q3_K_L.gguf) | Q3_K_L | 0.34GB |
+| [Qwen2-0.5B-KTO.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.IQ4_XS.gguf) | IQ4_XS | 0.33GB |
+| [Qwen2-0.5B-KTO.Q4_0.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q4_0.gguf) | Q4_0 | 0.33GB |
+| [Qwen2-0.5B-KTO.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.IQ4_NL.gguf) | IQ4_NL | 0.33GB |
+| [Qwen2-0.5B-KTO.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q4_K_S.gguf) | Q4_K_S | 0.36GB |
+| [Qwen2-0.5B-KTO.Q4_K.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q4_K.gguf) | Q4_K | 0.37GB |
+| [Qwen2-0.5B-KTO.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q4_K_M.gguf) | Q4_K_M | 0.37GB |
+| [Qwen2-0.5B-KTO.Q4_1.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q4_1.gguf) | Q4_1 | 0.35GB |
+| [Qwen2-0.5B-KTO.Q5_0.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q5_0.gguf) | Q5_0 | 0.37GB |
+| [Qwen2-0.5B-KTO.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q5_K_S.gguf) | Q5_K_S | 0.38GB |
+| [Qwen2-0.5B-KTO.Q5_K.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q5_K.gguf) | Q5_K | 0.39GB |
+| [Qwen2-0.5B-KTO.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q5_K_M.gguf) | Q5_K_M | 0.39GB |
+| [Qwen2-0.5B-KTO.Q5_1.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q5_1.gguf) | Q5_1 | 0.39GB |
+| [Qwen2-0.5B-KTO.Q6_K.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q6_K.gguf) | Q6_K | 0.47GB |
+| [Qwen2-0.5B-KTO.Q8_0.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf/blob/main/Qwen2-0.5B-KTO.Q8_0.gguf) | Q8_0 | 0.49GB |
+
+
+
+
+Original model description:
+---
+base_model: Qwen/Qwen2-0.5B-Instruct
+datasets: trl-lib/kto-mix-14k
+library_name: transformers
+model_name: Qwen2-0.5B-KTO
+tags:
+- generated_from_trainer
+- trl
+- kto
+licence: license
+---
+
+# Model Card for Qwen2-0.5B-KTO
+
+This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [trl-lib/kto-mix-14k](https://huggingface.co/datasets/trl-lib/kto-mix-14k) dataset.
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="qgallouedec/Qwen2-0.5B-KTO", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/huggingface/trl/runs/m4w4f1v9)
+
+This model was trained with KTO, a method introduced in [KTO: Model Alignment as Prospect Theoretic Optimization](https://huggingface.co/papers/2402.01306).
+
+### Framework versions
+
+- TRL: 0.12.0.dev0
+- Transformers: 4.46.0.dev0
+- Pytorch: 2.4.1
+- Datasets: 3.0.1
+- Tokenizers: 0.20.0
+
+## Citations
+
+Cite KTO as:
+
+```bibtex
+@article{ethayarajh2024kto,
+    title        = {{KTO: Model Alignment as Prospect Theoretic Optimization}},
+    author       = {Kawin Ethayarajh and Winnie Xu and Niklas Muennighoff and Dan Jurafsky and Douwe Kiela},
+    year         = 2024,
+    eprint       = {arXiv:2402.01306},
+}
+```
+
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```
+