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ConicCat
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Model Details
Model Provider
ConicCat
Model Tree
Base
google/gemma-4-31B
Quantized
this model
Input Modalities
Text
Image
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
python
from transformers import pipelinequestion = "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="None", device="cuda")output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]print(output["generated_text"])
This model was trained with DPO, a method introduced in Direct Preference Optimization: Your Language Model is Secretly a Reward Model.
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
@software{vonwerra2020trl,title = {{TRL: Transformers Reinforcement Learning}},author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},license = {Apache-2.0},url = {https://github.com/huggingface/trl},year = {2020}}