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sergiopaniego 
posted an update about 20 hours ago
Post
521
We just released TRL v0.26.0!

It comes packed with updates:
> Agent training with tools in GRPO
> New CISPO & SAPO losses + reasoning rewards
> vLLM quantization in colocate mode
> Dataset shuffling in SFT
> Lots of NEW examples
> Tons of fixes and documentation improvements

Hi, I was trying this in Google Colab and I got a memory issue. How much vram does this need? Sorry just new to this

Is there an easy way to know how much vram is required to train a model from the HF model card?

Thanks

from trl import SFTTrainer
from datasets import load_dataset

trainer = SFTTrainer(
    model="Qwen/Qwen3-0.6B",
    train_dataset=load_dataset("trl-lib/Capybara", split="train"),
)
trainer.train()```
·

Thanks for your interest!

The amount of memory required for training depends on many factors. If you're new, I'd recommend the following notebook that has a lot of details and runs for free on Colab:

https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_trl_lora_qlora.ipynb

We've even more examples that run on Colab: https://huggingface.co/docs/trl/example_overview#notebooks

Let us know how it goes!