hf-mem

npx skills add waybarrios/opencode-power-pack --skill hf-mem --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

`hf_mem` estimates the required memory for inference, including model weights and an optional KV cache, for Safetensors and GGUF for models on the Hugging Face Hub using HTTP Range requests i.e., without downloading or loading any weights locally. ## When to use? - User asks how much VRAM or memory a model needs to run - User wants to know if a model fits on their GPU or a given instance - User references a Hugging Face model ID or URL and asks about inference requirements ## What are the requirements? - `uv` installed (for `uvx`) - `HF_TOKEN` env var or `--hf-token` flag (for gated or private models only) ## How to run? Run with `--model-id` pointing to the Hugging Face Hub repository which will check that it either contains Safetensors (via `model.safetensors`, `model.safetensors.index.json` if sharded, or `model_index.json` for Diffusers) or GGUF model weights within. ```bash uvx hf-mem --model-id <model-id> --json-output ``` If the repository contains GGUF model weights in multiple precisions / quantizations, the estimations will be on a per-file basis, whereas for inference you won't load all of those but rather only a single precision. This being said, for GGUF you might as w

uvx hf-mem --model-id <model-id> --json-output
uvx hf-mem --model-id <model-id> --gguf-file <file-or-path> --json-output
uvx hf-mem --model-id MiniMaxAI/MiniMax-M2 --json-output
uvx hf-mem --model-id Qwen/Qwen-Image --json-output
uvx hf-mem --model-id google/embeddinggemma-300m --json-output
uvx hf-mem --model-id mistralai/Mistral-7B-v0.1 --experimental --json-output
uvx hf-mem --model-id unsloth/Qwen3.5-397B-A17B-GGUF --gguf-file Q4_K_M --experimental --json-output