--- title: "Nanbeige_Nanbeige4.3-33B-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f4b76bb4-5c97-4ae2-b688-4922bacb571c" updated: "2026-11-12" --- # Nanbeige\_Nanbeige4.3-33B-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF quantizations of Nanbeige4.3-33B, made by bartowski with an importance matrix, from about 21 GB to 66 GB. For running a 33B English and Chinese text model locally with llama.cpp. Nanbeige\_Nanbeige4.3-33B-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by bartowski ## About ### What it is This repository holds GGUF quantizations of Nanbeige4.3-33B, a 33B text model by Nanbeige. Bartowski made the files with llama.cpp and an importance matrix. Different quant levels trade file size against quality. The page lists English and Chinese as languages. ### Who it suits People who run large language models on their own hardware with llama.cpp or a tool built on it. The model takes text only and does not use speculative decoding, according to the card. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a 33B model locally instead of a cloud service | Load a mid-size quant and test it on prompts | | Local AI | Enthusiasts | Pick the quant that fits their memory | Take the file the card suggests when unsure | | Research | Model evaluators | Compare quant sizes against quality | Run the same prompts on a large and a small quant | | Bilingual work | Teams in English and Chinese | Use one model for both languages | Ask the same question in each language | You can skip it if you have limited memory. Even the file the card suggests is about 21 GB. ### Where to use it - llama.cpp and tools built on it, on Windows, macOS and Linux - Python with the Hugging Face tool, for downloading files - Tools that read the ChatML-style prompt format shown below | Use case | Who | What they get back | | --- | --- | --- | | Local chat | Enthusiasts | Text replies from a 33B model on your machine | | Tool calling tests | Developers | Replies that follow the tool-call format the card shows | | Quant comparison | Evaluators | A size and quality trade-off across many files | ### How to set it up Pick one file rather than the whole repository, because the full set is far larger than any one file. - Python and the Hugging Face tool for downloading - A llama.cpp based program to load the file - Memory for your chosen quant, from about 21 GB upward 1. Install the Hugging Face hub tool. 2. Choose a quant from the file list. 3. Download that file. The command below fetches the whole repository, so narrow it to one file. 4. Load the file in llama.cpp or a similar tool. ```bash pip install -U huggingface_hub hf download bartowski/Nanbeige_Nanbeige4.3-33B-GGUF ``` ### How to use it The card gives a prompt format with a system block, a user block and an assistant turn that opens with a think tag. It also shows a variant that adds tool definitions for function calling. ```text <|im_start|>system {system_prompt}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` | Quant | Size and role | | --- | --- | | bf16 | 66.24 GB, the full-precision weights, split into parts | | Q8\_0 | 35.20 GB, the largest quant, rarely needed | | Q6\_K\_L | 30.40 GB, a larger Q6\_K, near-perfect quality | | Q6\_K | 29.02 GB, near-perfect quality and recommended | | Q5\_K\_M | 25.07 GB, high quality and recommended | | Q5\_K\_S | 23.66 GB, high quality and recommended | | Q4\_K\_L | 22.34 GB, keeps more sensitive weights at higher precision | | Q4\_1 | 21.20 GB, a legacy format | | IQ4\_NL | 20.87 GB, like IQ4\_XS but a little larger | | Q4\_K\_M | 20.79 GB, the default pick for most uses | ### Example You download the Q4\_K\_M file, load it with the prompt format above and ask for a short summary of a paragraph you paste in. The model thinks first, then replies with the summary. ### For newcomers and experts A quant is a smaller copy of a model with its numbers stored in fewer bits. If you are unsure, start with Q4\_K\_M, which the card calls a good mix of size and performance. Move up to Q6\_K when you have memory to spare. The quants use an importance matrix, and the source checkpoint is listed at 33B parameters. The Q4\_K\_L file keeps more of the sensitive weights at higher precision, and the card notes Q4\_1 as a legacy format with better tokens per watt on Apple silicon. Bartowski made the files from the original model by Nanbeige. Pick a quant that fits your memory → Download that one file → Load it in llama.cpp or a similar tool → Send a prompt in the format shown File size in GB | Label | Value | | --- | --- | | bf16 | 66.24 GB | | Q8\_0 | 35.20 GB | | Q6\_K | 29.02 GB | | Q5\_K\_M | 25.07 GB | | Q4\_K\_M | 20.79 GB | ### Good to know - The file list shown is cut off, so more quants exist than the ones above. - The 4-bit weights are estimated at about 16.6 GB by the page's own record. - Input is text only. - License: apache-2.0. - Cost to run: none named, you use your own hardware. - Data it reaches: files downloaded from Hugging Face, and the text you give it. - Not for: image input, since the model takes text only. ## README ````markdown # Nanbeige_Nanbeige4.3-33B-GGUF GGUF quantizations of Nanbeige4.3-33B, made by bartowski with an importance matrix, from about 21 GB to 66 GB. For running a 33B English and Chinese text model locally with llama.cpp. Made by bartowski on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This repository holds GGUF quantizations of Nanbeige4.3-33B, a 33B text model by Nanbeige. Bartowski made the files with llama.cpp and an importance matrix. Different quant levels trade file size against quality. The page lists English and Chinese as languages. Who it suits: People who run large language models on their own hardware with llama.cpp or a tool built on it. The model takes text only and does not use speculative decoding, according to the card. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a 33B model locally instead of a cloud service | Load a mid-size quant and test it on prompts Local AI | Enthusiasts | Pick the quant that fits their memory | Take the file the card suggests when unsure Research | Model evaluators | Compare quant sizes against quality | Run the same prompts on a large and a small quant Bilingual work | Teams in English and Chinese | Use one model for both languages | Ask the same question in each language ``` You can skip it if you have limited memory. Even the file the card suggests is about 21 GB. Where to use it: - llama.cpp and tools built on it, on Windows, macOS and Linux - Python with the Hugging Face tool, for downloading files - Tools that read the ChatML-style prompt format shown below ```table Use case | Who | What they get back Local chat | Enthusiasts | Text replies from a 33B model on your machine Tool calling tests | Developers | Replies that follow the tool-call format the card shows Quant comparison | Evaluators | A size and quality trade-off across many files ``` How to set it up: Pick one file rather than the whole repository, because the full set is far larger than any one file. - Python and the Hugging Face tool for downloading - A llama.cpp based program to load the file - Memory for your chosen quant, from about 21 GB upward 1. Install the Hugging Face hub tool. 2. Choose a quant from the file list. 3. Download that file. The command below fetches the whole repository, so narrow it to one file. 4. Load the file in llama.cpp or a similar tool. ```bash pip install -U huggingface_hub hf download bartowski/Nanbeige_Nanbeige4.3-33B-GGUF ``` How to use it: The card gives a prompt format with a system block, a user block and an assistant turn that opens with a think tag. It also shows a variant that adds tool definitions for function calling. ```text <|im_start|>system {system_prompt}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ```table Quant | Size and role bf16 | 66.24 GB, the full-precision weights, split into parts Q8_0 | 35.20 GB, the largest quant, rarely needed Q6_K_L | 30.40 GB, a larger Q6_K, near-perfect quality Q6_K | 29.02 GB, near-perfect quality and recommended Q5_K_M | 25.07 GB, high quality and recommended Q5_K_S | 23.66 GB, high quality and recommended Q4_K_L | 22.34 GB, keeps more sensitive weights at higher precision Q4_1 | 21.20 GB, a legacy format IQ4_NL | 20.87 GB, like IQ4_XS but a little larger Q4_K_M | 20.79 GB, the default pick for most uses ``` Example: You download the Q4_K_M file, load it with the prompt format above and ask for a short summary of a paragraph you paste in. The model thinks first, then replies with the summary. For newcomers and experts: A quant is a smaller copy of a model with its numbers stored in fewer bits. If you are unsure, start with Q4_K_M, which the card calls a good mix of size and performance. Move up to Q6_K when you have memory to spare. The quants use an importance matrix, and the source checkpoint is listed at 33B parameters. The Q4_K_L file keeps more of the sensitive weights at higher precision, and the card notes Q4_1 as a legacy format with better tokens per watt on Apple silicon. Bartowski made the files from the original model by Nanbeige. ```flow Pick a quant that fits your memory Download that one file Load it in llama.cpp or a similar tool Send a prompt in the format shown ``` ```chart Title: File size in GB bf16: 66.24 GB Q8_0: 35.20 GB Q6_K: 29.02 GB Q5_K_M: 25.07 GB Q4_K_M: 20.79 GB ``` Good to know: - The file list shown is cut off, so more quants exist than the ones above. - The 4-bit weights are estimated at about 16.6 GB by the page's own record. - Input is text only. - License: apache-2.0. - Cost to run: none named, you use your own hardware. - Data it reaches: files downloaded from Hugging Face, and the text you give it. - Not for: image input, since the model takes text only. ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 33.1B - Weights: about 16.6 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 151,191 - Likes: 33 - License: apache-2.0 - Languages: en, zh - Based on: Nanbeige/Nanbeige4.3-33B - Fine-tunes: 1 - Tags: gguf, nanbeige, text-generation, imatrix, conversational ## Links - Model: https://huggingface.co/bartowski/Nanbeige_Nanbeige4.3-33B-GGUF - Files: https://huggingface.co/bartowski/Nanbeige_Nanbeige4.3-33B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download bartowski/Nanbeige_Nanbeige4.3-33B-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [bartowski/Nanbeige_Nanbeige4.3-33B-GGUF on Hugging Face](https://huggingface.co/bartowski/Nanbeige_Nanbeige4.3-33B-GGUF) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Nanbeige\_Nanbeige4.3-33B-GGUF (this page) | 151,191 | 33 | 33.1B | apache-2.0 | 2026-10-25 | | [Nanbeige4.2-3B by Nanbeige](https://mindket.com/p/4b18411b-9c39-46d8-88db-2a4a8ea71f5f) | 96,886 | 860 | 4.2B | apache-2.0 | 2026-09-11 | | [Nanbeige4.3-33B](https://mindket.com/p/c7975e8b-eb6f-497a-a398-2028206aa2a0) | 21,843 | 1,077 | 33.1B | apache-2.0 | 2026-10-28 | | [Nanbeige4.3-33B-INT4](https://mindket.com/p/6ce5c80d-8374-46fe-9a56-8640a8a3b951) | 749 | 10 | 33.1B | apache-2.0 | 2026-10-31 | | [Nanbeige4.3-33B-GGUF-UltraOptimised-Curated-Protium](https://mindket.com/p/6b811fb1-998e-42e8-b70a-1da4c5451c9a) | 767 | 6 | 33.1B | apache-2.0 | 2026-10-28 | | [Sharp-Spark-X2.5-4B-GGUF](https://mindket.com/p/d4d90f81-4886-4de3-ab95-d64c344a708c) | 875,346 | 164 | 4.1B | apache-2.0 | 2026-11-04 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · Steven \| SelfHostedSI · 2026-11-01](https://bsky.app/profile/selfhostedsi.bsky.social/post/3mzavcmx4dz2y): File list: huggingface.co/bartowski/N... ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-25 - First published: 2026-10-25 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open bartowski/Nanbeige_Nanbeige4.3-33B-GGUF on Hugging Face](https://huggingface.co/bartowski/Nanbeige_Nanbeige4.3-33B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f4b76bb4-5c97-4ae2-b688-4922bacb571c) - [Its page on Mindket](https://mindket.com/p/f4b76bb4-5c97-4ae2-b688-4922bacb571c)