{"id":243,"date":"2026-01-09T18:49:21","date_gmt":"2026-01-09T18:49:21","guid":{"rendered":"https:\/\/profitiraj.ba\/quantizing-llms-step-by-step-converting-fp16-models-to-gguf\/"},"modified":"2026-01-09T18:49:21","modified_gmt":"2026-01-09T18:49:21","slug":"quantizing-llms-step-by-step-changing-fp16-fashions-to-gguf","status":"publish","type":"post","link":"https:\/\/profitiraj.ba\/bs\/quantizing-llms-step-by-step-changing-fp16-fashions-to-gguf\/","title":{"rendered":"Quantizing LLMs Step-by-Step: Changing FP16 Fashions to GGUF"},"content":{"rendered":"<div class=\"af1ed52b2bea0830e8f3a5b7a892ea1d\" data-index=\"1\" style=\"float: none; margin:10px 0 10px 0; text-align:center;\">\n<div data-type=\"_mgwidget\" data-widget-id=\"2030988\">\r\n<\/div>\r\n<script>(function(w,q){w[q]=w[q]||[];w[q].push([\"_mgc.load\"])})(window,\"_mgq\");\r\n<\/script>\n<\/div>\n<p><\/p>\n<div id=\"\">\n<p>On this article, you&#8217;ll learn the way quantization shrinks giant language fashions and how one can convert an FP16 checkpoint into an environment friendly GGUF file you&#8217;ll be able to share and run domestically.<\/p>\n<p>Matters we are going to cowl embody:<\/p>\n<ul>\n<li>What precision varieties (FP32, FP16, 8-bit, 4-bit) imply for mannequin dimension and velocity<\/li>\n<li>The right way to use <code>huggingface_hub<\/code> to fetch a mannequin and authenticate<\/li>\n<li>The right way to convert to GGUF with <code>llama.cpp<\/code> and add the consequence to <strong>Hugging Face<\/strong><\/li>\n<\/ul>\n<p>And away we go.<\/p>\n<div style=\"width: 810px\" class=\"wp-caption aligncenter\"><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/machinelearningmastery.com\/wp-content\/uploads\/2026\/01\/mlm-How-to-Quantize-Your-Own-Model-From-FP16-to-GGUF.png\" alt=\"How to Quantize Your Own Model (From FP16 to GGUF)\" width=\"800\" height=\"706\"\/><\/p>\n<p class=\"wp-caption-text\">Quantizing LLMs Step-by-Step: Changing FP16 Fashions to GGUF<br \/>Picture by Writer<\/p>\n<\/div>\n<h2>Introduction<\/h2>\n<p>Massive language fashions like LLaMA, Mistral, and Qwen have billions of parameters that demand lots of reminiscence and compute energy. For instance, working LLaMA 7B in full precision can require over 12 GB of VRAM, making it impractical for a lot of customers. You possibly can test the main points on this <strong>Hugging Face dialogue<\/strong>. Don\u2019t fear about what \u201cfull precision\u201d means but; we\u2019ll break it down quickly. The primary thought is that this: these fashions are too massive to run on customary {hardware} with out assist. Quantization is that assist.<\/p>\n<p>Quantization permits unbiased researchers and hobbyists to run giant fashions on private computer systems by shrinking the scale of the mannequin with out severely impacting efficiency. On this information, we\u2019ll discover how quantization works, what completely different precision codecs imply, after which stroll by quantizing a pattern FP16 mannequin right into a GGUF format and importing it to <strong>Hugging Face<\/strong>.<\/p>\n<h2>What Is Quantization?<\/h2>\n<p>At a really primary stage, quantization is about making a mannequin smaller with out breaking it. Massive language fashions are made up of billions of numerical values known as <strong>weights<\/strong>. These numbers management how strongly completely different components of the community affect one another when producing an output. By default, these weights are saved utilizing high-precision codecs corresponding to FP32 or FP16, which implies each quantity takes up lots of reminiscence, and when you could have billions of them, issues get out of hand in a short time. Take a single quantity like <code>2.31384<\/code>. In FP32, that one quantity alone makes use of 32 bits of reminiscence. Now think about storing billions of numbers like that. That is why a 7B mannequin can simply take round 28 GB in FP32 and about 14 GB even in FP16. For many laptops and GPUs, that\u2019s already an excessive amount of. <\/p>\n<p><strong>Quantization fixes this by saying: we don\u2019t really need that a lot precision anymore.<\/strong> As a substitute of storing <code>2.31384<\/code> precisely, we retailer one thing near it utilizing fewer bits. Perhaps it turns into <code>2.3<\/code> or a close-by integer worth beneath the hood. The quantity is barely much less correct, however the mannequin nonetheless behaves the identical in follow. Neural networks can tolerate these small errors as a result of the ultimate output will depend on billions of calculations, not a single quantity. Small variations common out, very like picture compression reduces file dimension with out ruining how the picture seems. However the payoff is large. <strong>A mannequin that wants 14 GB in FP16 can typically run in about 7 GB with 8-bit quantization, and even round 4 GB with 4-bit quantization.<\/strong> That is what makes it potential to run giant language fashions domestically as an alternative of counting on costly servers.<\/p>\n<p>After quantizing, we frequently retailer the mannequin in a unified file format. One fashionable format is <strong>GGUF<\/strong>created by Georgi Gerganov (writer of <code>llama.cpp<\/code>). <strong>GGUF is a single-file format that features each the quantized weights and helpful metadata<\/strong>. It\u2019s optimized for fast loading and inference on CPUs or different light-weight runtimes. GGUF additionally helps a number of quantization varieties (like Q4_0, Q8_0) and works effectively on CPUs and low-end GPUs. Hopefully, this clarifies each the idea and the motivation behind quantization. Now let\u2019s transfer on to writing some code.<\/p>\n<h2>Step-by-Step: Quantizing a Mannequin to GGUF<\/h2>\n<h3>1. Putting in Dependencies and Logging to Hugging Face<\/h3>\n<p>Earlier than downloading or changing any mannequin, we have to set up the required Python packages and authenticate with <strong>Hugging Face<\/strong>. We\u2019ll use <strong>huggingface_hub<\/strong>, <strong>Transformers<\/strong>and <strong>SentencePiece<\/strong>. This ensures we are able to entry public or gated fashions with out errors:<\/p>\n<div id=\"urvanov-syntax-highlighter-695f97bb777b5678431053\" class=\"urvanov-syntax-highlighter-syntax crayon-theme-classic urvanov-syntax-highlighter-font-monaco urvanov-syntax-highlighter-os-pc print-yes notranslate\" data-settings=\" minimize scroll-mouseover disable-anim\" style=\" margin-top: 12px; margin-bottom: 12px; font-size: 12px !important; line-height: 15px !important;\">\n<p><textarea wrap=\"soft\" class=\"urvanov-syntax-highlighter-plain print-no\" data-settings=\"dblclick\" readonly=\"readonly\" style=\"-moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4; font-size: 12px !important; line-height: 15px !important;\"><\/p>\n<p>!pip set up -U huggingface_hub transformers sentencepiece -q&#13;<br \/>\n&#13;<br \/>\nfrom huggingface_hub import login&#13;<br \/>\nlogin()<\/textarea><\/p>\n<div class=\"urvanov-syntax-highlighter-main\" style=\"\">\n<table class=\"crayon-table\">\n<tr class=\"urvanov-syntax-highlighter-row\">\n<td class=\"crayon-nums \" data-settings=\"show\">\n<\/td>\n<td class=\"urvanov-syntax-highlighter-code\">\n<div class=\"crayon-pre\" style=\"font-size: 12px !important; line-height: 15px !important; -moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4;\">\n<p><span class=\"crayon-o\">!<\/span><span class=\"crayon-e\">pip <\/span><span class=\"crayon-v\">set up<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-i\">U<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-e\">huggingface_hub <\/span><span class=\"crayon-e\">transformers <\/span><span class=\"crayon-v\">sentencepiece<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-i\">q<\/span><\/p>\n<p><span class=\"crayon-e\">from <\/span><span class=\"crayon-e\">huggingface_hub <\/span><span class=\"crayon-e\">import <\/span><span class=\"crayon-e\">login<\/span><\/p>\n<p><span class=\"crayon-e\">login<\/span><span class=\"crayon-sy\">(<\/span><span class=\"crayon-sy\">)<\/span><\/p>\n<\/div>\n<\/td>\n<\/tr>\n<\/table><\/div>\n<\/p><\/div>\n<h3>2. Downloading a Pre-trained Mannequin<\/h3>\n<p>We&#8217;ll choose a small FP16 mannequin from <strong>Hugging Face<\/strong>. Right here we use TinyLlama 1.1B, which is sufficiently small to run in Colab however nonetheless offers a very good demonstration. Utilizing Python, we are able to obtain it with <code>huggingface_hub<\/code>:<\/p>\n<div id=\"urvanov-syntax-highlighter-695f97bb777c2424768626\" class=\"urvanov-syntax-highlighter-syntax crayon-theme-classic urvanov-syntax-highlighter-font-monaco urvanov-syntax-highlighter-os-pc print-yes notranslate\" data-settings=\" minimize scroll-mouseover disable-anim\" style=\" margin-top: 12px; margin-bottom: 12px; font-size: 12px !important; line-height: 15px !important;\">\n<p><textarea wrap=\"soft\" class=\"urvanov-syntax-highlighter-plain print-no\" data-settings=\"dblclick\" readonly=\"readonly\" style=\"-moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4; font-size: 12px !important; line-height: 15px !important;\"><\/p>\n<p>from huggingface_hub import snapshot_download&#13;<br \/>\n&#13;<br \/>\nmodel_id = &#8220;TinyLlama\/TinyLlama-1.1B-Chat-v1.0&#8243;&#13;<br \/>\nsnapshot_download(&#13;<br \/>\n    repo_id=model_id,&#13;<br \/>\n    local_dir=&#8221;model_folder&#8221;,&#13;<br \/>\n    local_dir_use_symlinks=False&#13;<br \/>\n)<\/textarea><\/p>\n<div class=\"urvanov-syntax-highlighter-main\" style=\"\">\n<table class=\"crayon-table\">\n<tr class=\"urvanov-syntax-highlighter-row\">\n<td class=\"crayon-nums \" data-settings=\"show\">\n<\/td>\n<td class=\"urvanov-syntax-highlighter-code\">\n<div class=\"crayon-pre\" style=\"font-size: 12px !important; line-height: 15px !important; -moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4;\">\n<p><span class=\"crayon-e\">from <\/span><span class=\"crayon-e\">huggingface_hub <\/span><span class=\"crayon-e\">import <\/span><span class=\"crayon-e\">snapshot_download<\/span><\/p>\n<p><span class=\"crayon-v\">model_id<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">=<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-s\">&#8220;TinyLlama\/TinyLlama-1.1B-Chat-v1.0&#8221;<\/span><\/p>\n<p><span class=\"crayon-e\">snapshot_download<\/span><span class=\"crayon-sy\">(<\/span><\/p>\n<p><span class=\"crayon-h\">    <\/span><span class=\"crayon-v\">repo_id<\/span><span class=\"crayon-o\">=<\/span><span class=\"crayon-v\">model_id<\/span><span class=\"crayon-sy\">,<\/span><\/p>\n<p><span class=\"crayon-h\">    <\/span><span class=\"crayon-v\">local_dir<\/span><span class=\"crayon-o\">=<\/span><span class=\"crayon-s\">&#8220;model_folder&#8221;<\/span><span class=\"crayon-sy\">,<\/span><\/p>\n<p><span class=\"crayon-h\">    <\/span><span class=\"crayon-v\">local_dir_use_symlinks<\/span><span class=\"crayon-o\">=<\/span><span class=\"crayon-t\">False<\/span><\/p>\n<p><span class=\"crayon-sy\">)<\/span><\/p>\n<\/div>\n<\/td>\n<\/tr>\n<\/table><\/div>\n<\/p><\/div>\n<p>This command saves the mannequin recordsdata into the <code>model_folder<\/code> listing. You possibly can exchange <code>model_id<\/code> with any Hugging Face mannequin ID that you just need to quantize. (If wanted, you can too use <code>AutoModel.from_pretrained<\/code> with <code>torch.float16<\/code> to load it first, however <code>snapshot_download<\/code> is simple for grabbing the recordsdata.)<\/p>\n<h3>3. Setting Up the Conversion Instruments<\/h3>\n<p>Subsequent, we clone the <strong>name.cpp<\/strong> repository, which comprises the conversion scripts. In Colab:<\/p>\n<div id=\"urvanov-syntax-highlighter-695f97bb777c6590017437\" class=\"urvanov-syntax-highlighter-syntax crayon-theme-classic urvanov-syntax-highlighter-font-monaco urvanov-syntax-highlighter-os-pc print-yes notranslate\" data-settings=\" minimize scroll-mouseover disable-anim\" style=\" margin-top: 12px; margin-bottom: 12px; font-size: 12px !important; line-height: 15px !important;\">\n<p><textarea wrap=\"soft\" class=\"urvanov-syntax-highlighter-plain print-no\" data-settings=\"dblclick\" readonly=\"readonly\" style=\"-moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4; font-size: 12px !important; line-height: 15px !important;\"><\/p>\n<p>!git clone https:\/\/github.com\/ggml-org\/llama.cpp&#13;<br \/>\n!pip set up -r llama.cpp\/necessities.txt -q<\/textarea><\/p>\n<div class=\"urvanov-syntax-highlighter-main\" style=\"\">\n<table class=\"crayon-table\">\n<tr class=\"urvanov-syntax-highlighter-row\">\n<td class=\"crayon-nums \" data-settings=\"show\">\n<\/td>\n<td class=\"urvanov-syntax-highlighter-code\">\n<div class=\"crayon-pre\" style=\"font-size: 12px !important; line-height: 15px !important; -moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4;\">\n<p><span class=\"crayon-o\">!<\/span><span class=\"crayon-e\">git <\/span><span class=\"crayon-r\">clone<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-v\">https<\/span><span class=\"crayon-o\">:<\/span><span class=\"crayon-c\">\/\/github.com\/ggml-org\/llama.cpp<\/span><\/p>\n<p><span class=\"crayon-o\">!<\/span><span class=\"crayon-e\">pip <\/span><span class=\"crayon-v\">set up<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-i\">r<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-v\">llama<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">cpp<\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">necessities<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">txt<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-v\">q<\/span><\/p>\n<\/div>\n<\/td>\n<\/tr>\n<\/table><\/div>\n<\/p><\/div>\n<p>This offers you entry to <code>convert_hf_to_gguf.py<\/code>. The Python necessities guarantee you could have all wanted libraries to run the script.<\/p>\n<h3>4. Changing the Mannequin to GGUF with Quantization<\/h3>\n<p>Now, run the conversion script, specifying the enter folder, output filename, and quantization sort. We&#8217;ll use <code>q8_0<\/code> (8-bit quantization). This may roughly halve the reminiscence footprint of the mannequin:<\/p>\n<div id=\"urvanov-syntax-highlighter-695f97bb777c8430805155\" class=\"urvanov-syntax-highlighter-syntax crayon-theme-classic urvanov-syntax-highlighter-font-monaco urvanov-syntax-highlighter-os-pc print-yes notranslate\" data-settings=\" minimize scroll-mouseover disable-anim\" style=\" margin-top: 12px; margin-bottom: 12px; font-size: 12px !important; line-height: 15px !important;\">\n<p><textarea wrap=\"soft\" class=\"urvanov-syntax-highlighter-plain print-no\" data-settings=\"dblclick\" readonly=\"readonly\" style=\"-moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4; font-size: 12px !important; line-height: 15px !important;\"><\/p>\n<p>!python3 llama.cpp\/convert_hf_to_gguf.py \/content material\/model_folder &#13;<br \/>\n    &#8211;outfile \/content material\/tinyllama-1.1b-chat.Q8_0.gguf &#13;<br \/>\n    &#8211;outtype q8_0<\/textarea><\/p>\n<div class=\"urvanov-syntax-highlighter-main\" style=\"\">\n<table class=\"crayon-table\">\n<tr class=\"urvanov-syntax-highlighter-row\">\n<td class=\"crayon-nums \" data-settings=\"show\">\n<\/td>\n<td class=\"urvanov-syntax-highlighter-code\">\n<div class=\"crayon-pre\" style=\"font-size: 12px !important; line-height: 15px !important; -moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4;\">\n<p><span class=\"crayon-o\">!<\/span><span class=\"crayon-e\">python3 <\/span><span class=\"crayon-v\">llama<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">cpp<\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">convert_hf_to_gguf<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">py<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">content material<\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">mannequin<\/span><span class=\"crayon-sy\">_<\/span>folder<span class=\"crayon-h\"> <\/span><span class=\"crayon-sy\"><\/span><\/p>\n<p><span class=\"crayon-h\">    <\/span><span class=\"crayon-o\">&#8212;<\/span><span class=\"crayon-v\">outfile<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">content material<\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">tinyllama<\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-cn\">1.1b<\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-v\">chat<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">Q8_0<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-i\">gguf<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-sy\"><\/span><\/p>\n<p><span class=\"crayon-h\">    <\/span><span class=\"crayon-o\">&#8212;<\/span><span class=\"crayon-e\">outtype <\/span><span class=\"crayon-v\">q8_0<\/span><\/p>\n<\/div>\n<\/td>\n<\/tr>\n<\/table><\/div>\n<\/p><\/div>\n<p>Right here <code>\/content material\/model_folder<\/code> is the place we downloaded the mannequin, <code>\/content material\/tinyllama-1.1b-chat.Q8_0.gguf<\/code> is the output GGUF file, and the <code>--outtype q8_0<\/code> flag means \u201cquantize to 8-bit.\u201d The script masses the FP16 weights, converts them into 8-bit values, and writes a single GGUF file. This file is now a lot smaller and prepared for inference with GGUF-compatible instruments.<\/p>\n<div id=\"urvanov-syntax-highlighter-695f97bb777cb031495109\" class=\"urvanov-syntax-highlighter-syntax crayon-theme-classic urvanov-syntax-highlighter-font-monaco urvanov-syntax-highlighter-os-pc print-yes notranslate\" data-settings=\" minimize scroll-mouseover disable-anim\" style=\" margin-top: 12px; margin-bottom: 12px; font-size: 12px !important; line-height: 15px !important;\">\n<p><textarea wrap=\"soft\" class=\"urvanov-syntax-highlighter-plain print-no\" data-settings=\"dblclick\" readonly=\"readonly\" style=\"-moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4; font-size: 12px !important; line-height: 15px !important;\"><\/p>\n<p>Output:&#13;<br \/>\nINFO:gguf.gguf_writer:Writing the next recordsdata:&#13;<br \/>\nINFO:gguf.gguf_writer:\/content material\/tinyllama-1.1b-chat.Q8_0.gguf: n_tensors = 201, total_size = 1.2G&#13;<br \/>\nWriting: 100% 1.17G\/1.17G [00:26&lt;00:00, 44.5Mbyte\/s]&#13;<br \/>\nINFO:hf-to-gguf:Mannequin efficiently exported to \/content material\/tinyllama-1.1b-chat.Q8_0.gguf<\/textarea><\/p>\n<div class=\"urvanov-syntax-highlighter-main\" style=\"\">\n<table class=\"crayon-table\">\n<tr class=\"urvanov-syntax-highlighter-row\">\n<td class=\"crayon-nums \" data-settings=\"show\">\n<\/td>\n<td class=\"urvanov-syntax-highlighter-code\">\n<div class=\"crayon-pre\" style=\"font-size: 12px !important; line-height: 15px !important; -moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4;\">\n<p><span class=\"crayon-v\">Output<\/span><span class=\"crayon-o\">:<\/span><\/p>\n<p><span class=\"crayon-v\">INFO<\/span><span class=\"crayon-o\">:<\/span><span class=\"crayon-v\">gguf<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">gguf_writer<\/span><span class=\"crayon-o\">:<\/span><span class=\"crayon-e\">Writing <\/span><span class=\"crayon-e\">the <\/span><span class=\"crayon-e\">following <\/span><span class=\"crayon-v\">recordsdata<\/span><span class=\"crayon-o\">:<\/span><\/p>\n<p><span class=\"crayon-v\">INFO<\/span><span class=\"crayon-o\">:<\/span><span class=\"crayon-v\">gguf<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">gguf_writer<\/span><span class=\"crayon-o\">:<\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">content material<\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">tinyllama<\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-cn\">1.1b<\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-v\">chat<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">Q8_0<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">gguf<\/span><span class=\"crayon-o\">:<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-v\">n_tensors<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">=<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-cn\">201<\/span><span class=\"crayon-sy\">,<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-v\">total_size<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">=<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-cn\">1.2G<\/span><\/p>\n<p><span class=\"crayon-v\">Writing<\/span><span class=\"crayon-o\">:<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-cn\">100<\/span><span class=\"crayon-o\">%<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-cn\">1.17G<\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-cn\">1.17G<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-sy\">[<\/span><span class=\"crayon-cn\">00<\/span><span class=\"crayon-o\">:<\/span><span class=\"crayon-cn\">26<\/span><span class=\"crayon-o\">&lt;<\/span><span class=\"crayon-cn\">00<\/span><span class=\"crayon-o\">:<\/span><span class=\"crayon-cn\">00<\/span><span class=\"crayon-sy\">,<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-cn\">44.5Mbyte<\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">s<\/span><span class=\"crayon-sy\">]<\/span><\/p>\n<p><span class=\"crayon-v\">INFO<\/span><span class=\"crayon-o\">:<\/span><span class=\"crayon-v\">hf<\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-st\">to<\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-v\">gguf<\/span><span class=\"crayon-o\">:<\/span><span class=\"crayon-e\">Mannequin <\/span><span class=\"crayon-e\">efficiently <\/span><span class=\"crayon-e\">exported <\/span><span class=\"crayon-st\">to<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">content material<\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">tinyllama<\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-cn\">1.1b<\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-v\">chat<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">Q8_0<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">gguf<\/span><\/p>\n<\/div>\n<\/td>\n<\/tr>\n<\/table><\/div>\n<\/p><\/div>\n<p>You possibly can confirm the output:<\/p>\n<div id=\"urvanov-syntax-highlighter-695f97bb777ce384533368\" class=\"urvanov-syntax-highlighter-syntax crayon-theme-classic urvanov-syntax-highlighter-font-monaco urvanov-syntax-highlighter-os-pc print-yes notranslate\" data-settings=\" minimize scroll-mouseover disable-anim\" style=\" margin-top: 12px; margin-bottom: 12px; font-size: 12px !important; line-height: 15px !important;\">\n<p><textarea wrap=\"soft\" class=\"urvanov-syntax-highlighter-plain print-no\" data-settings=\"dblclick\" readonly=\"readonly\" style=\"-moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4; font-size: 12px !important; line-height: 15px !important;\"><\/p>\n<p>!ls -lh \/content material\/tinyllama-1.1b-chat.Q8_0.gguf<\/textarea><\/p>\n<div class=\"urvanov-syntax-highlighter-main\" style=\"\">\n<table class=\"crayon-table\">\n<tr class=\"urvanov-syntax-highlighter-row\">\n<td class=\"crayon-nums \" data-settings=\"show\">\n<\/td>\n<td class=\"urvanov-syntax-highlighter-code\">\n<div class=\"crayon-pre\" style=\"font-size: 12px !important; line-height: 15px !important; -moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4;\">\n<p><span class=\"crayon-o\">!<\/span><span class=\"crayon-v\">ls<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-v\">lh<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">content material<\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">tinyllama<\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-cn\">1.1b<\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-v\">chat<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">Q8_0<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">gguf<\/span><\/p>\n<\/div>\n<\/td>\n<\/tr>\n<\/table><\/div>\n<\/p><\/div>\n<p>You must see a file a number of GB in dimension, diminished from the unique FP16 mannequin.<\/p>\n<div id=\"urvanov-syntax-highlighter-695f97bb777d2137045957\" class=\"urvanov-syntax-highlighter-syntax crayon-theme-classic urvanov-syntax-highlighter-font-monaco urvanov-syntax-highlighter-os-pc print-yes notranslate\" data-settings=\" minimize scroll-mouseover disable-anim\" style=\" margin-top: 12px; margin-bottom: 12px; font-size: 12px !important; line-height: 15px !important;\">\n<p><textarea wrap=\"soft\" class=\"urvanov-syntax-highlighter-plain print-no\" data-settings=\"dblclick\" readonly=\"readonly\" style=\"-moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4; font-size: 12px !important; line-height: 15px !important;\"><\/p>\n<p>-rw-r&#8211;r&#8211; 1 root root 1.1G Dec 30 20:23 \/content material\/tinyllama-1.1b-chat.Q8_0.gguf<\/textarea><\/p>\n<div class=\"urvanov-syntax-highlighter-main\" style=\"\">\n<table class=\"crayon-table\">\n<tr class=\"urvanov-syntax-highlighter-row\">\n<td class=\"crayon-nums \" data-settings=\"show\">\n<\/td>\n<td class=\"urvanov-syntax-highlighter-code\">\n<div class=\"crayon-pre\" style=\"font-size: 12px !important; line-height: 15px !important; -moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4;\">\n<p><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-v\">rw<\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-v\">r<\/span><span class=\"crayon-o\">&#8212;<\/span><span class=\"crayon-v\">r<\/span><span class=\"crayon-o\">&#8212;<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-cn\">1<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-e\">root <\/span><span class=\"crayon-i\">root<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-cn\">1.1G<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-i\">Dec<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-cn\">30<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-cn\">20<\/span><span class=\"crayon-o\">:<\/span><span class=\"crayon-cn\">23<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">content material<\/span><span class=\"crayon-o\">\/<\/span><span class=\"crayon-v\">tinyllama<\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-cn\">1.1b<\/span><span class=\"crayon-o\">&#8211;<\/span><span class=\"crayon-v\">chat<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">Q8_0<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-v\">gguf<\/span><\/p>\n<\/div>\n<\/td>\n<\/tr>\n<\/table><\/div>\n<\/p><\/div>\n<h3>5. Importing the Quantized Mannequin to Hugging Face<\/h3>\n<p>Lastly, you&#8217;ll be able to publish the GGUF mannequin so others can simply obtain and use it utilizing the <code>huggingface_hub<\/code> Python library:<\/p>\n<div id=\"urvanov-syntax-highlighter-695f97bb777d5417372368\" class=\"urvanov-syntax-highlighter-syntax crayon-theme-classic urvanov-syntax-highlighter-font-monaco urvanov-syntax-highlighter-os-pc print-yes notranslate\" data-settings=\" minimize scroll-mouseover disable-anim\" style=\" margin-top: 12px; margin-bottom: 12px; font-size: 12px !important; line-height: 15px !important;\">\n<p><textarea wrap=\"soft\" class=\"urvanov-syntax-highlighter-plain print-no\" data-settings=\"dblclick\" readonly=\"readonly\" style=\"-moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4; font-size: 12px !important; line-height: 15px !important;\"><\/p>\n<p>from huggingface_hub import HfApi&#13;<br \/>\n&#13;<br \/>\napi = HfApi()&#13;<br \/>\nrepo_id = &#8220;kanwal-mehreen18\/tinyllama-1.1b-gguf&#8221;&#13;<br \/>\napi.create_repo(repo_id, exist_ok=True)&#13;<br \/>\n&#13;<br \/>\napi.upload_file(&#13;<br \/>\n    path_or_fileobj=&#8221;\/content material\/tinyllama-1.1b-chat.Q8_0.gguf&#8221;,&#13;<br \/>\n    path_in_repo=&#8221;tinyllama-1.1b-chat.Q8_0.gguf&#8221;,&#13;<br \/>\n    repo_id=repo_id&#13;<br \/>\n)<\/textarea><\/p>\n<div class=\"urvanov-syntax-highlighter-main\" style=\"\">\n<table class=\"crayon-table\">\n<tr class=\"urvanov-syntax-highlighter-row\">\n<td class=\"crayon-nums \" data-settings=\"show\">\n<\/td>\n<td class=\"urvanov-syntax-highlighter-code\">\n<div class=\"crayon-pre\" style=\"font-size: 12px !important; line-height: 15px !important; -moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4;\">\n<p><span class=\"crayon-e\">from <\/span><span class=\"crayon-e\">huggingface_hub <\/span><span class=\"crayon-e\">import <\/span><span class=\"crayon-e\">HfApi<\/span><\/p>\n<p><span class=\"crayon-v\">api<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">=<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-e\">HfApi<\/span><span class=\"crayon-sy\">(<\/span><span class=\"crayon-sy\">)<\/span><\/p>\n<p><span class=\"crayon-v\">repo_id<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-o\">=<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-s\">&#8220;kanwal-mehreen18\/tinyllama-1.1b-gguf&#8221;<\/span><\/p>\n<p><span class=\"crayon-v\">api<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-e\">create_repo<\/span><span class=\"crayon-sy\">(<\/span><span class=\"crayon-v\">repo_id<\/span><span class=\"crayon-sy\">,<\/span><span class=\"crayon-h\"> <\/span><span class=\"crayon-v\">exist_ok<\/span><span class=\"crayon-o\">=<\/span><span class=\"crayon-t\">True<\/span><span class=\"crayon-sy\">)<\/span><\/p>\n<p><span class=\"crayon-v\">api<\/span><span class=\"crayon-sy\">.<\/span><span class=\"crayon-e\">upload_file<\/span><span class=\"crayon-sy\">(<\/span><\/p>\n<p><span class=\"crayon-h\">    <\/span><span class=\"crayon-v\">path_or_fileobj<\/span><span class=\"crayon-o\">=<\/span><span class=\"crayon-s\">&#8220;\/content material\/tinyllama-1.1b-chat.Q8_0.gguf&#8221;<\/span><span class=\"crayon-sy\">,<\/span><\/p>\n<p><span class=\"crayon-h\">    <\/span><span class=\"crayon-v\">path_in_repo<\/span><span class=\"crayon-o\">=<\/span><span class=\"crayon-s\">&#8220;tinyllama-1.1b-chat.Q8_0.gguf&#8221;<\/span><span class=\"crayon-sy\">,<\/span><\/p>\n<p><span class=\"crayon-h\">    <\/span><span class=\"crayon-v\">repo_id<\/span><span class=\"crayon-o\">=<\/span><span class=\"crayon-v\">repo<\/span><span class=\"crayon-sy\">_<\/span>id<\/p>\n<p><span class=\"crayon-sy\">)<\/span><\/p>\n<\/div>\n<\/td>\n<\/tr>\n<\/table><\/div>\n<\/p><\/div>\n<p>This creates a brand new repository (if it doesn\u2019t exist) and uploads your quantized GGUF file. Anybody can now load it with <code>llama.cpp<\/code>, <code>llama-cpp-python<\/code>or <code>Ollama<\/code>. You possibly can entry the quantized GGUF file that we created right here.<\/p>\n<h2>Wrapping Up<\/h2>\n<p>By following the steps above, you&#8217;ll be able to take any supported <strong>Hugging Face<\/strong> mannequin, quantize it (e.g. to 4-bit or 8-bit), and put it aside as GGUF. Then push it to Hugging Face to share or deploy. This makes it simpler than ever to compress and use giant language fashions on on a regular basis {hardware}.<\/p>\n<\/p><\/div>\n<!--CusAds0-->\n<div style=\"font-size: 0px; height: 0px; line-height: 0px; margin: 0; padding: 0; clear: both;\"><\/div>","protected":false},"excerpt":{"rendered":"<p>On this article, you&#8217;ll learn the way quantization shrinks giant language fashions and how one can convert an FP16 checkpoint into an environment friendly GGUF file you&#8217;ll be able to share and run domestically. Matters we are going to cowl embody: What precision varieties (FP32, FP16, 8-bit, 4-bit) imply for mannequin dimension and velocity The [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":245,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"https:\/\/machinelearningmastery.com\/wp-content\/uploads\/2026\/01\/mlm-How-to-Quantize-Your-Own-Model-From-FP16-to-GGUF.png","fifu_image_alt":"","footnotes":""},"categories":[1],"tags":[46,47,49,44,48,43,45],"class_list":["post-243","post","type-post","status-publish","format-standard","has-post-thumbnail","category-world-news","tag-converting","tag-fp16","tag-gguf","tag-llms","tag-models","tag-quantizing","tag-stepbystep"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Quantizing LLMs Step-by-Step: Changing FP16 Fashions to GGUF - Profitiraj.ba<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/machinelearningmastery.com\/quantizing-llms-step-by-step-converting-fp16-models-to-gguf\/\" \/>\n<meta property=\"og:locale\" content=\"bs_BA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Quantizing LLMs Step-by-Step: Changing FP16 Fashions to GGUF - Profitiraj.ba\" \/>\n<meta property=\"og:description\" content=\"On this article, you&#8217;ll learn the way quantization shrinks giant language fashions and how one can convert an FP16 checkpoint into an environment friendly GGUF file you&#8217;ll be able to share and run domestically. 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