GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
Hahmdong
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
Hahmdong
Model Tree
Base
Hahmdong/SPUPER-qwen3-4b-owlier-addition
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="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 SFT.
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 Gallou{\'e}dec},year = 2020,journal = {GitHub repository},publisher = {GitHub},howpublished = {\url{https://github.com/huggingface/trl}}}