alvarobartt/hf-mem

• highreadme#1

  Reposition README opening to emphasize unique value for Hugging Face memory estimation

  Why:

  CURRENT

  > [!WARNING]
  > `hf-mem` is still experimental and therefore subject to major changes across releases, so please keep in mind that breaking changes may occur until v1.0.0.
  
  `hf-mem` is a CLI to estimate inference memory requirements for Hugging Face models, written in Python.

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  **`hf-mem` is a lightweight Python CLI designed specifically to accurately estimate inference memory requirements for Hugging Face models (Transformers, Diffusers, Sentence Transformers) *before* deployment.** Avoid out-of-memory errors and optimize resource allocation by predicting the memory footprint for any model on the Hugging Face Hub, leveraging Safetensors and GGUF metadata via HTTP Range requests.
  
  > [!WARNING]
  > `hf-mem` is still experimental and therefore subject to major changes across releases, so please keep in mind that breaking changes may occur until v1.0.0.

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• hightopics#2

  Add specific topics related to ML memory estimation and resource planning

  Why:

  CURRENT

  gguf, hf-extension, huggingface, safetensors

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  gguf, hf-extension, huggingface, safetensors, memory-estimation, ml-inference, resource-management, deep-learning, python-cli, model-deployment

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• mediumreadme#3

  Add a 'Why hf-mem?' or 'Comparison' section to the README

  Why:

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  ## Why `hf-mem`?
  
  Unlike general-purpose memory profilers (e.g., `memory_profiler`, `nvidia-smi`) or large ML frameworks (e.g., Hugging Face Accelerate, PyTorch), `hf-mem` is specifically designed for **pre-deployment inference memory estimation of Hugging Face models**. It provides a lightweight, automatic, and non-intrusive way to predict memory usage by directly analyzing model metadata (Safetensors, GGUF) via HTTP Range requests, without needing to load the full model or run actual inference. This allows you to proactively plan your GPU/CPU resources and avoid out-of-memory errors before you even start training or deploying.

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

• CATEGORY QUERY

  How to calculate memory usage for Hugging Face models before deployment?

  you: not recommended
  AI recommended (in order):

  1. huggingface/accelerate (huggingface/accelerate)
  2. PyTorch (pytorch/pytorch)
  3. Pytorch-OpCounter (Lyken17/pytorch-OpCounter)
  4. PyTorch Profiler (pytorch/pytorch)
  5. memory_profiler (pythonprofilers/memory_profiler)
  6. nvidia-smi

  AI recommended 6 alternatives but never named alvarobartt/hf-mem. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  Python CLI to predict memory footprint for running Hugging Face models?

  you: not recommended
  AI recommended (in order):

  1. Hugging Face Transformers Library (huggingface/transformers)
  2. accelerate (huggingface/accelerate)
  3. nvidia-smi
  4. memory_profiler (pythonprofilers/memory_profiler)
  5. PyTorch profiler (pytorch/pytorch)
  6. psutil (giampaolo/psutil)

  AI recommended 6 alternatives but never named alvarobartt/hf-mem. 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 alvarobartt/hf-mem?

  pass
  ✓AI named alvarobartt/hf-mem explicitly

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

• If a team adopts alvarobartt/hf-mem in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named alvarobartt/hf-mem 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 alvarobartt/hf-mem solve, and who is the primary audience?

  pass
  ✓AI named alvarobartt/hf-mem explicitly

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