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+
+# GGUF
+
+[GGUF](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md) is a file format used to store models for inference with [GGML](https://github.com/ggerganov/ggml), a fast and lightweight inference framework written in C and C++. GGUF is a single-file format containing the model metadata and tensors.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/gguf-spec.png"/>
+</div>
+
+The GGUF format also supports many quantized data types (refer to [quantization type table](https://hf.co/docs/hub/en/gguf#quantization-types) for a complete list of supported quantization types) which saves a significant amount of memory, making inference with large models like Whisper and Llama feasible on local and edge devices.
+
+Transformers supports loading models stored in the GGUF format for further training or finetuning. The GGUF checkpoint is **dequantized to fp32** where the full model weights are available and compatible with PyTorch.
+
+> [!TIP]
+> Models that support GGUF include Llama, Mistral, Qwen2, Qwen2Moe, Phi3, Bloom, Falcon, StableLM, GPT2, Starcoder2, and [more](https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/ggml.py)
+
+Add the `gguf_file` parameter to [`~PreTrainedModel.from_pretrained`] to specify the GGUF file to load.
+
+```py
+# pip install gguf
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF"
+filename = "tinyllama-1.1b-chat-v1.0.Q6_K.gguf"
+
+dtype = torch.float32 # could be torch.float16 or torch.bfloat16 too
+tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
+model = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename, dtype=dtype)
+```
+
+Once you're done tinkering with the model, save and convert it back to the GGUF format with the [convert-hf-to-gguf.py](https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py) script.
+
+```py
+tokenizer.save_pretrained("directory")
+model.save_pretrained("directory")
+
+!python ${path_to_llama_cpp}/convert-hf-to-gguf.py ${directory}
+```