LLM gradual quantization: FP16 model to GGUF conversion

This article describes how to reduce large language models through quantization and convert FP16 checkpoints into efficient GGUF files that can be shared and executed locally.

Topics covered include:

• What does precision type (FP32, FP16, 8-bit, 4-bit) mean on model size and speed?
• How to use huggingface_hub Get the model and authenticate it
• How to convert to GGUF with llama.cpp and upload the results hug face

And then we leave.

LLM gradual quantization: FP16 model to GGUF conversion
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introduction

Large language models such as LLaMA, Mistral, and Qwen have billions of parameters that require large amounts of memory and computational power. For example, running LLaMA 7B at full precision can require more than 12 GB of VRAM, making it impractical for many users. Details can be found here A face-to-face discussion. Don’t worry yet about what “full precision” means. I’ll try to disassemble it soon. The main ideas are: These models are too large to run on standard hardware without assistance. Quantization can help with that.

Quantization allows independent researchers and hobbyists to run large models on personal computers by reducing the size of the model without significantly impacting performance. This guide explains how quantization works, what the different precision formats mean, and explains how to quantize a sample FP16 model to GGUF format and upload it. hug face.

What is quantization?

At a very basic level, quantization is about making a model smaller without breaking it. A large language model is made up of billions of numbers called . weight. These numbers control how strongly different parts of the network influence each other when producing output. By default, these weights are stored using a high precision format such as FP32 or FP16. This means that every number takes up a lot of memory, and when you have billions of numbers, it can quickly become unmanageable. Take a single number like 2.31384. In FP32, that single number uses 32 bits of memory. Imagine storing billions of numbers like this. This is why the 7B model easily takes up around 28 GB in FP32 and around 14 GB in FP16 as well. For most laptops and GPUs, that’s already too much.

Quantization fixes this by saying, “We don’t actually need that much precision anymore.” instead of storing 2.31384 To be exact, use fewer bits to save something close to it. Maybe it will happen 2.3 Or an integer value near the interior. The model actually behaves the same, although the numerical accuracy is slightly less. Neural networks can tolerate these small errors because the final output depends on billions of calculations rather than a single number. Just as image compression reduces the file size without sacrificing the visual appearance of the image, small differences are averaged out. However, the cost is high. A model that requires 14 GB in FP16 can often run in about 7 GB with 8-bit quantization, or about 4 GB with 4-bit quantization. This allows you to run large language models locally without relying on expensive servers.

After quantization, models are often saved in a uniform file format. One popular format is GGUFcreated by (author of) Georgy Gerganov. llama.cpp). GGUF is a single file format that contains both quantized weights and useful metadata.. Optimized for quick loading and inference on CPUs or other lightweight runtimes. GGUF also supports multiple quantization types (Q4_0, Q8_0, etc.) and works well on CPUs and low-end GPUs. This will hopefully clarify both the concept and the motivation behind quantization. Now let’s move on to writing the code.

Step-by-step: Quantize the model to GGUF

1. Installing dependencies and logging to Hugging Face

Before you can download or convert a model, you must install the required Python packages and authenticate with Python. hug face. use hug face hub, transformersand sentence piece. This ensures that you can access public or gated models without errors.

2. Download pre-trained model

Select the smaller FP16 model from. hug face. Here we use TinyLlama 1.1B. This is small enough to run in Colab, but still provides a good demonstration. Using Python, you can download it as follows: huggingface_hub:

This command saves the model file to model_folder directory. can be exchanged model_id Use the hug face model ID you want to quantize. (as needed, AutoModel.from_pretrained and torch.float16 First I load it, snapshot_download Retrieving files is easy. )

3. Setting up the conversion tool

Next, create a clone. llama.cpp Repository containing conversion scripts. For collaboration:

This will give you access to convert_hf_to_gguf.py. Python requirements ensure that you have all the necessary libraries to run your script.

4. Converting the model to GGUF with quantization

Now run the conversion script, specifying the input folder, output file name, and quantization type. use q8_0 (8-bit quantization). This roughly halves the memory usage of the model.

here /content/model_folder This is where you downloaded the model. /content/tinyllama-1.1b-chat.Q8_0.gguf is the output GGUF file, --outtype q8_0 flag means “quantize to 8 bits”. The script loads the FP16 weights, converts them to 8-bit values, and writes a single GGUF file. This file is now much smaller and can now be inferred with GGUF compatible tools.

You can check the output.

You should see a file that is several GB smaller in size than the original FP16 model.

5. Upload the quantized model to Hugface

Finally, you can publish your GGUF model so that others can easily download and use it. huggingface_hub Python libraries:

This will create a new repository (if it doesn’t exist) and upload the quantized GGUF file. Anyone can now load llama.cpp, llama-cpp-pythonor Ollama. You can access the quantized GGUF file you created here.

summary

You can run anything supported by following the steps above hug face Create a model, quantize it (e.g. to 4 bits or 8 bits), and save it as a GGUF. Then push it to Hugging Face to share or deploy it. This makes it easier than ever to compress and use large language models on everyday hardware.