jayzou3773

Qwen1.5-MOE-rebuttal-condenser-static

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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="jayzou3773/Qwen1.5-MOE-rebuttal-condenser-static", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.16.0.dev0
Transformers: 4.49.0
Pytorch: 2.6.0
Datasets: 4.8.5
Tokenizers: 0.21.4

Citations

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}}
}

Model provider

jayzou3773

Model tree

Base

HectorHe/Qwen1.5-MOE-aux-free-sft-math7k-1e-3-gamma-1epoch

Fine-tuned

this model

Modalities

Input

Text

Output

Text

Pricing

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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="jayzou3773/Qwen1.5-MOE-rebuttal-condenser-static", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.16.0.dev0
Transformers: 4.49.0
Pytorch: 2.6.0
Datasets: 4.8.5
Tokenizers: 0.21.4

Citations

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}}
}

Qwen1.5-MOE-rebuttal-condenser-static

Get help setting up a custom Dedicated Endpoints.

README

Quick start

Training procedure

Framework versions

Citations

Explore FriendliAI today

README

Quick start

Training procedure

Framework versions

Citations