RichardErkhov
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README.md
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Quantization made by Richard Erkhov.
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[Github](https://github.com/RichardErkhov)
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[Discord](https://discord.gg/pvy7H8DZMG)
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[Request more models](https://github.com/RichardErkhov/quant_request)
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self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7 - GGUF
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- Model creator: https://huggingface.co/RyanYr/
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- Original model: https://huggingface.co/RyanYr/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7/
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q2_K.gguf) | Q2_K | 1.39GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q3_K_S.gguf) | Q3_K_S | 1.59GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q3_K.gguf) | Q3_K | 1.73GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q3_K_M.gguf) | Q3_K_M | 1.73GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q3_K_L.gguf) | Q3_K_L | 1.85GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.IQ4_XS.gguf) | IQ4_XS | 1.91GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q4_0.gguf) | Q4_0 | 1.99GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.IQ4_NL.gguf) | IQ4_NL | 2.0GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q4_K_S.gguf) | Q4_K_S | 2.0GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q4_K.gguf) | Q4_K | 2.09GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q4_K_M.gguf) | Q4_K_M | 2.09GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q4_1.gguf) | Q4_1 | 2.18GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q5_0.gguf) | Q5_0 | 2.37GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q5_K_S.gguf) | Q5_K_S | 2.37GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q5_K.gguf) | Q5_K | 2.41GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q5_K_M.gguf) | Q5_K_M | 2.41GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q5_1.gguf) | Q5_1 | 2.55GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q6_K.gguf) | Q6_K | 2.76GB |
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| [self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7-gguf/blob/main/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7.Q8_0.gguf) | Q8_0 | 3.58GB |
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Original model description:
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---
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base_model: RyanYr/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2
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library_name: transformers
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model_name: self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7
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tags:
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- generated_from_trainer
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- trl
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- dpo
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licence: license
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---
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# Model Card for self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7
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This model is a fine-tuned version of [RyanYr/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2](https://huggingface.co/RyanYr/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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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?"
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generator = pipeline("text-generation", model="RyanYr/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter2-only2nd-6e-7", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<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/yyr/huggingface/runs/c3qe0974)
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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).
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### Framework versions
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- TRL: 0.12.0.dev0
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- Transformers: 4.45.2
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- Pytorch: 2.4.0
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- Datasets: 3.0.1
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- Tokenizers: 0.20.1
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## Citations
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Cite DPO as:
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```bibtex
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@inproceedings{rafailov2023direct,
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title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
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author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
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year = 2023,
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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},
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url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
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editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
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}
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```
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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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},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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