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Convert GGUF to PTH

Verified by Adam B.· reviewed by FE Editorial ·updated Jul 14, 2026
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Is it possible to convert GGUF to PTH?
Yes - GGUF converts to PTH.

Use a Python script built on the gguf library's GGUFReader and dequantize() to read each tensor, upcast it to F32/F16, remap the names to their original PyTorch keys, and call torch.save(). The catch: a GGUF that was quantized (Q4, Q5, etc.) never regains the precision it lost, so the resulting weights are dequantized approximations, not the original model.

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Tested on macOS, Windows & Linux
Last verified Sep 2026

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Why convert GGUF to PTH?

People convert .gguf to .pth when they want to fine-tune or re-quantize a model in the PyTorch ecosystem but only have the GGUF build. A GGUF stores metadata (like a config) plus tensor data (like a state dict), so mapping it back to a PyTorch state dict is possible for supported architectures. It is a recovery path, not a lossless one.

How to convert GGUF to PTH

gguf Python library + PyTorch OPEN-SOURCE

Install gguf and torch, then loop over GGUFReader(path).tensors, run gguf.quants.dequantize() on each, and torch.save(state_dict, "model.pth"). You must remap GGUF tensor names back to the model's original keys.

gguf_to_safetensors then convert OPEN-SOURCE

Run gguf_to_safetensors to dequantize to a .safetensors file, then load it with safetensors.torch.load_file and re-save via torch.save() to get a .pth.

llama.cpp reverse tooling FREE

llama.cpp's gguf reader exposes the tensors; script the dequantize step and write a PyTorch state dict. Only architectures llama.cpp fully supports will round-trip.

About these formats

.GGUF GGML Universal Format File

A .gguf file is a GGML Universal Format File, the binary container used by llama.cpp and related tools to store a large language model as a single file. It holds the model weights,…

Open .GGUF details →
.PTH Python Path Configuration File

A .pth file is most commonly a Python path configuration file - a tiny plain-text file that tells Python which extra directories to add to its module search path when it starts. You do not…

Open .PTH details →

Quality & what to watch

  • Quantized GGUF weights are lossy - dequantizing gives an approximation, never the original FP16/FP32 values.
  • Tensor name mapping is architecture-specific; unsupported models will not remap and the conversion fails.
  • The output .pth is a bare state dict with no model class, so you still need matching PyTorch model code to load it.

Frequently asked questions

Can I get the original model back exactly?
No if the GGUF was quantized. Dequantization reconstructs full-precision tensors from lossy data, so small numerical differences remain.
Do I need a GPU?
No. Reading and dequantizing tensors runs on CPU; a GPU only helps if you later use the model.
Why does my conversion produce wrong tensor names?
Name mapping is per-architecture. If the model isn't in the script's mapping table, keys won't match PyTorch and loading fails.
Which is better, .pth or .safetensors?
.safetensors is safer to share (no pickle code execution) and loads faster, but .pth is the native PyTorch state-dict format for training.