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 [Visualize in Weights & Biases](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}} } ```