rfvasile/linalgzero-sft
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How to use rfvasile/trainer_output with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rfvasile/trainer_output", dtype="auto")How to use rfvasile/trainer_output with Unsloth Studio:
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rfvasile/trainer_output to start chatting
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rfvasile/trainer_output to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rfvasile/trainer_output to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="rfvasile/trainer_output",
max_seq_length=2048,
)This model is a fine-tuned version of unsloth/Qwen3-4B-Base on the atomwalk12/linalgzero-sft dataset. It has been trained using TRL.
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="atomwalk12/trainer_output", 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:
@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}}
}