Model Card for None

This model is a fine-tuned version of Qwen/Qwen3-8B. It has been trained using TRL.

Quick start

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="None", 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: 0.12.0
• Transformers: 4.51.1
• Pytorch: 2.5.1+cu124
• Datasets: 3.6.0
• Tokenizers: 0.21.1

Citations

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 Gallouédec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}

Provenance (FAIR cluster backup, 2026-09-14)

• Source dir: /checkpoint/comem/shannons/latent-cot/ckpts-w32/20260102-004850~gist~Qwen3-8B~ot3-1.2m-50k-converted~bs128~lr4e-5~epoch5~l18000~gz5+d-sentence+cross_gist+input+wrap_gist_token+wrap_gist_token_to_special_tokens+use_latent_ema+iter2+weight0.2+latent_ema_version_v3+mask-v2+keep_math_span
• Launch: train/commands/week32/01-01-iterations-latent-ema-vx-alpha-final.sh (iterations=2, version=v3, weight=0.2, gist_size=5, delimiter=sentence)
• Base model: Qwen/Qwen3-8B · Dataset: shannons/ot3-1.2m-50k-converted
• Paper row: Paper Table 2: LaTexT m=5 sentence (previously non-public row)