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Alvaro Bartolome

alvarobartt
huggingface
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https://alvarobartt.com

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machine learning @huggingface (inference + cloud)

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liked a model about 1 hour ago
unsloth/Qwen3.6-35B-A3B-GGUF
upvoted a changelog about 4 hours ago
Introducing Kernels
reacted to sergiopaniego's post with 🤗 about 4 hours ago
Earlier this month, Apple introduced Simple Self-Distillation: a fine-tuning method that improves models on coding tasks just by sampling from the model and training on its own outputs with plain cross-entropy And… it's already supported in TRL, built by Kashif Rasul. you can really feel the pace of development in the team 🐎 Paper by Ruixiang ZHANG, He Bai, Huangjie Zheng, Navdeep Jaitly, Ronan Collobert, Yizhe Zhang at Apple 🍎 How it works: the model generates completions at a training-time temperature (T_train) with top_k/top_p truncation, then fine-tunes on them with plain cross-entropy. no labels or verifier needed You can try it right away with this ready-to-run example (Qwen3-4B on rStar-Coder): https://github.com/huggingface/trl/blob/main/trl/experimental/ssd/ssd.py or benchmark a checkpoint with the eval script: https://github.com/huggingface/trl/blob/main/trl/experimental/ssd/ssd_eval.py One neat insight from the paper: T_train and T_eval compose into an effective T_eff = T_train × T_eval, so a broad band of configs works well. even very noisy samples still help Want to dig deeper? Paper: https://huggingface.co/papers/2604.01193 Trainer docs: https://huggingface.co/docs/trl/main/en/ssd_trainer
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