Scaling Laws for Neural Language Models
April 13, 2026
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"The best way to predict the future is to invent it." — Alan Kay
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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "meta-llama/Llama-3-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
prompt = "The future of AI is"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Table
Model | Parameters | MMLU | HellaSwag |
|---|---|---|---|
LLaMA 3 8B | 8B | 66.6 | 82.0 |
LLaMA 3 70B | 70B | 79.5 | 87.3 |
GPT-4 | ~1.8T | 86.4 | 95.3 |
Mixtral 8x7B | 46.7B | 70.6 | 84.4 |
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Scientific references
The editor supports academic citations. The Transformer architecture [vaswani2017] revolutionized natural language processing by introducing the self-attention mechanism. Large-scale pretraining [devlin2019] demonstrated that unsupervised objectives on massive corpora produce powerful general-purpose representations.
More recently, scaling laws [kaplan2020] have shown predictable relationships between model size, dataset size, and performance.
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Details
The model was fine-tuned using LoRA adapters with rank 16 on 4× A100 GPUs. Training took approximately 12 hours on the full dataset. We used a cosine learning rate schedule with warm-up over the first 10% of steps.
All experiments were conducted with mixed-precision (bf16) training. Results may vary slightly with different random seeds.
If you have a very large neural network and you train it on a very large dataset, you get very good results. It really is that simple.
Do not use these scaling estimates for production capacity planning without accounting for inference overhead and memory constraints.
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Scaling laws were first systematically studied in the context of neural machine translation before being generalized to language models.
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The concept of Scaling law has become central to modern AI research. Early work* suggested these relationships hold across modalities.
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