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Add 3 new evidence files from modern open-source sources: - karpathy_recipe_training_nn_2019.md: Karpathy's training recipe blog post - nanochat_deepwiki_llm_pretraining_2026.md: 320+ HP sweeps for GPT-2-scale pretraining - sanh_simple_considerations_hf_2021.md: HuggingFace NLP debugging notes Add update-to-data ratio diagnostic to refs/diagnostics.md (target ~1e-3). Add LLM pretraining gap note to SKILL.md intro linking the new sources. Add tanh saturation % to logging checklist.
62 lines
2.6 KiB
Markdown
62 lines
2.6 KiB
Markdown
# Simple Considerations for Simple People Building Fancy Neural Networks
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**Source:** Victor Sanh, Hugging Face Blog, February 25, 2021
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**URL:** https://huggingface.co/blog/simple-considerations
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**Author:** Victor Sanh (Hugging Face research scientist, author of DistilBERT)
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---
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## Core practices (overlaps heavily with Karpathy 2019 recipe)
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**Data first:**
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> "the very first step of building a neural network is to put aside machine learning and simply focus on your data"
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**Overfit test:**
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> "it is a good habit when you think you have finished implementing to overfit a small batch of examples (16 for instance). If your implementation is (nearly) correct, your model will be able to overfit and remember these examples by displaying a 0-loss (make sure you remove any form of regularization such as weight decay)."
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**Baselines:**
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> "Start as simple as possible to get a sense of the difficulty of your task and how well standard baselines would perform."
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> "it is sometimes hard to understand if your performance comes from a bug in your model/code or is simply limited by your model's expressiveness"
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---
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## NLP-specific: tokenization warning
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> "when you work with language, have a serious look at the outputs of the tokenizers. I can't count the number of lost hours I spent trying to reproduce results (and sometimes my own old results) because something went wrong with the tokenization."
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---
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## Common implementation errors listed
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- Wrong indexing ("really the worst")
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- Forgetting `model.eval()` or `model.zero_grad()`
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- Preprocessing errors
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- Loss receiving wrong argument type (probabilities vs. logits)
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- Uniform constant initialization (breaks symmetry)
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- Parameters not called in forward pass (no gradients)
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- Learning rate stuck at 0
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- Suboptimal input truncation
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---
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## HP tuning advice
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> "there is no point of launching 1000 runs with different hyperparameters: compare a couple of runs with different hyperparameters to get an idea of which hyperparameters have the highest impact"
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> "random over a reasonably manually defined grid search is still a tough-to-beat baseline" [re: Bayesian vs random search]
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---
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## Embeddings freezing (NLP, pre-trained LM fine-tuning)
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> "in my experience working with pre-trained language models, freezing the embeddings modules to their pre-trained values doesn't affect much the fine-tuning task performance while considerably speeding up the training."
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Credence ~65-70% -- specific domain claim, lacks ablation study reference.
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---
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## External links from this post
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- "Checklist for debugging neural networks" -- Cecelia Shao (Towards Data Science)
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- "A recipe for Training Neural Networks" -- Karpathy
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