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-DPO - GGUF - Model creator: https://huggingface.co/trl-lib/ - Original model: https://huggingface.co/trl-lib/Qwen2-0.5B-DPO/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Qwen2-0.5B-DPO.Q2_K.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q2_K.gguf) | Q2_K | 0.32GB | | [Qwen2-0.5B-DPO.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.IQ3_XS.gguf) | IQ3_XS | 0.32GB | | [Qwen2-0.5B-DPO.IQ3_S.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.IQ3_S.gguf) | IQ3_S | 0.32GB | | [Qwen2-0.5B-DPO.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q3_K_S.gguf) | Q3_K_S | 0.32GB | | [Qwen2-0.5B-DPO.IQ3_M.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.IQ3_M.gguf) | IQ3_M | 0.32GB | | [Qwen2-0.5B-DPO.Q3_K.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q3_K.gguf) | Q3_K | 0.33GB | | [Qwen2-0.5B-DPO.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q3_K_M.gguf) | Q3_K_M | 0.33GB | | [Qwen2-0.5B-DPO.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q3_K_L.gguf) | Q3_K_L | 0.34GB | | [Qwen2-0.5B-DPO.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.IQ4_XS.gguf) | IQ4_XS | 0.33GB | | [Qwen2-0.5B-DPO.Q4_0.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q4_0.gguf) | Q4_0 | 0.33GB | | [Qwen2-0.5B-DPO.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.IQ4_NL.gguf) | IQ4_NL | 0.33GB | | [Qwen2-0.5B-DPO.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q4_K_S.gguf) | Q4_K_S | 0.36GB | | [Qwen2-0.5B-DPO.Q4_K.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q4_K.gguf) | Q4_K | 0.37GB | | [Qwen2-0.5B-DPO.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q4_K_M.gguf) | Q4_K_M | 0.37GB | | [Qwen2-0.5B-DPO.Q4_1.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q4_1.gguf) | Q4_1 | 0.35GB | | [Qwen2-0.5B-DPO.Q5_0.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q5_0.gguf) | Q5_0 | 0.37GB | | [Qwen2-0.5B-DPO.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q5_K_S.gguf) | Q5_K_S | 0.38GB | | [Qwen2-0.5B-DPO.Q5_K.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q5_K.gguf) | Q5_K | 0.39GB | | [Qwen2-0.5B-DPO.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q5_K_M.gguf) | Q5_K_M | 0.39GB | | [Qwen2-0.5B-DPO.Q5_1.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q5_1.gguf) | Q5_1 | 0.39GB | | [Qwen2-0.5B-DPO.Q6_K.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q6_K.gguf) | Q6_K | 0.47GB | | [Qwen2-0.5B-DPO.Q8_0.gguf](https://huggingface.co/RichardErkhov/trl-lib_-_Qwen2-0.5B-DPO-gguf/blob/main/Qwen2-0.5B-DPO.Q8_0.gguf) | Q8_0 | 0.49GB | Original model description: --- base_model: Qwen/Qwen2-0.5B-Instruct datasets: trl-lib/Capybara-Preferences library_name: transformers model_name: dpo-qwen2 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for dpo-qwen2 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/Capybara-Preferences](https://huggingface.co/datasets/trl-lib/Capybara-Preferences) 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/dpo-qwen2", 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/8g0pylqi) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.45.0.dev0 - Pytorch: 2.4.1 - Datasets: 3.0.0 - Tokenizers: 0.19.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` 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}} } ```