MrYxJ/calculate-flops.pytorch

• highreadme#1

  Reposition the README Introduction to highlight LLM/Hugging Face focus

  Why:

  CURRENT

  This tool(calflops) is designed to compute the theoretical amount of FLOPs(floating-point operations)、MACs(multiply-add operations) and Parameters in all various neural networks, such as Linear、 CNN、 RNN、 GCN、**Transformer(Bert、LlaMA etc Large Language Model)**，even including **any custom models** via ```torch.nn.function.*``` as long as based on the Pytorch implementation.

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  Calflops is the easiest and most convenient tool for calculating FLOPs, MACs, and Parameters in PyTorch models, especially for large language models (LLMs) and models from the Hugging Face platform. It supports various neural networks including Linear, CNN, RNN, GCN, and Transformers (like Bert, LLaMA), as well as any custom models based on PyTorch.

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• mediumabout#2

  Update the repository description to emphasize LLM and Hugging Face support

  Why:

  CURRENT

  The calflops is designed to calculate FLOPs、MACs and Parameters in all various neural networks, such as Linear、 CNN、 RNN、 GCN、Transformer(Bert、LlaMA etc Large Language Model)

  COPY-PASTE FIX

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  Calflops is an easy-to-use tool for calculating FLOPs, MACs, and Parameters in PyTorch neural networks, with a special focus on large language models (LLMs) and Hugging Face models. It supports various architectures like Linear, CNN, RNN, GCN, and Transformers (e.g., Bert, LLaMA).

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• mediumtopics#3

  Add `huggingface` and `llm` to repository topics

  Why:

  CURRENT

  calflops, flops-counter, large-language-models, pytorch

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  calflops, flops-counter, large-language-models, pytorch, huggingface, llm

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How to accurately measure computational cost and parameters for PyTorch neural network models?

  you: not recommended
  AI recommended (in order):

  1. PyTorch-OpCounter (torchstat) (sovrasov/flops-counter.pytorch)
  2. thop (Lyken116/pytorch-OpCounter)
  3. fvcore (from Detectron2) (facebookresearch/fvcore)
  4. torchinfo (TylerYep/torchinfo)
  5. ptflops (sukhov-alex/ptflops)
  6. DeepSpeed (flops_profiler) (microsoft/DeepSpeed)

  AI recommended 6 alternatives but never named MrYxJ/calculate-flops.pytorch. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  Need a tool to analyze FLOPs and parameter usage in large language models built with PyTorch.

  you: not recommended
  AI recommended (in order):

  1. PyTorch profiler
  2. thop
  3. fvcore
  4. torchinfo
  5. deepspeed.profiler
  6. NVIDIA Nsight Systems

  AI recommended 6 alternatives but never named MrYxJ/calculate-flops.pytorch. This is the gap to close.

  Show full AI answer

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of MrYxJ/calculate-flops.pytorch?

  pass
  ⚠AI did not name MrYxJ/calculate-flops.pytorch — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts MrYxJ/calculate-flops.pytorch in production, what risks or prerequisites should they evaluate first?

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  ✓AI named MrYxJ/calculate-flops.pytorch explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo MrYxJ/calculate-flops.pytorch solve, and who is the primary audience?

  pass
  ⚠AI did not name MrYxJ/calculate-flops.pytorch — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?