CoolFace
Modelpublic

RichardErkhov/RyanYr_-_self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5-gguf

sourceHugging Faceupdated 2y agoView on Hugging Face
0likes795downloads
Model Card

Quantization made by Richard Erkhov.

Github

Discord

Request more models

self-correctLlama-3.2-3B-InstructmetaMathQAdpoiter5 - GGUF

  • —Model creator: https://huggingface.co/RyanYr/
  • —Original model: https://huggingface.co/RyanYr/self-correctLlama-3.2-3B-InstructmetaMathQAdpoiter5/
NameQuant methodSize
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q2_K.ggufQ2_K1.39GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.IQ3_XS.ggufIQ3_XS1.53GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.IQ3_S.ggufIQ3_S1.59GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q3_K_S.ggufQ3KS1.59GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.IQ3_M.ggufIQ3_M1.65GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q3_K.ggufQ3_K1.73GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q3_K_M.ggufQ3KM1.73GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q3_K_L.ggufQ3KL1.85GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.IQ4_XS.ggufIQ4_XS1.91GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q4_0.ggufQ4_01.99GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.IQ4_NL.ggufIQ4_NL2.0GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q4_K_S.ggufQ4KS2.0GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q4_K.ggufQ4_K2.09GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q4_K_M.ggufQ4KM2.09GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q4_1.ggufQ4_12.18GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q5_0.ggufQ5_02.37GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q5_K_S.ggufQ5KS2.37GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q5_K.ggufQ5_K2.41GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q5_K_M.ggufQ5KM2.41GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q5_1.ggufQ5_12.55GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q6_K.ggufQ6_K2.76GB
self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5.Q8_0.ggufQ8_03.58GB

Original model description: --- basemodel: RyanYr/self-correctLlama-3.2-3B-InstructmetaMathQAdpoiter4 libraryname: transformers modelname: self-correctLlama-3.2-3B-InstructmetaMathQAdpo_iter5 tags:

  • —generatedfromtrainer
  • —trl
  • —dpo licence: license ---

Model Card for self-correctLlama-3.2-3B-InstructmetaMathQAdpoiter5

This model is a fine-tuned version of RyanYr/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter4. It has been trained using 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="RyanYr/self-correct_Llama-3.2-3B-Instruct_metaMathQA_dpo_iter5", 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"/>

This model was trained with DPO, a method introduced in Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

Framework versions

  • —TRL: 0.12.0.dev0
  • —Transformers: 4.45.2
  • —Pytorch: 2.4.0
  • —Datasets: 3.0.1
  • —Tokenizers: 0.20.1

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}}
}

Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more quants, at much higher speed, than I would otherwise be able to.