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swadeshb
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Model Details
Model Provider
swadeshb
Model Tree
Base
Qwen/Qwen3-4B
Fine-tuned
this model
Input Modalities
Text
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="swadeshb/Qwen3-4B-scopd", 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 Distillation, a method introduced in On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes.
Cite Distillation as:
bibtex
@inproceedings{agarwal2024on-policy,title = {{On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes}},author = {Rishabh Agarwal and Nino Vieillard and Yongchao Zhou and Piotr Stanczyk and Sabela Ramos Garea and Matthieu Geist and Olivier Bachem},year = 2024,booktitle = {The Twelfth International Conference on Learning Representations, {ICLR} 2024, Vienna, Austria, May 7-11, 2024},publisher = {OpenReview.net},url = {https://openreview.net/forum?id=3zKtaqxLhW},}
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}}