--- title: "KnowLine-4B-Gen5.5-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4a03f33e-ec52-4528-97f2-3edeae847aca" updated: "2026-11-13" --- # KnowLine-4B-Gen5.5-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Static GGUF files of PelaAI's KnowLine 4B model in sizes from 2.1 GB to 8.8 GB, with English and Chinese listed. For people who run a small model on their own computer. KnowLine-4B-Gen5.5-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by mradermacher ## About ### What it is This repository holds GGUF files that mradermacher produced from the KnowLine 4B model by PelaAI. Each file trades size for quality, so you choose one that fits your memory. The model has 4.3B parameters and lists English and Chinese. The card does not say what the base model was tuned to do. ### Who it suits It suits people who want a small model running on their own computer in a compact file. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Home use | Local AI users | Run a small model on their own computer | Load the Q4\_K\_M file of 2.9 GB in a GGUF app | | Software | Developers | Test a quantized model on local hardware | Compare Q4\_K\_S and Q8\_0 for speed and quality | | Research | Quantization researchers | Study how quality changes between sizes | Run one prompt set against several quant types | If you want the original full-precision weights, you can skip it and use the model it was made from. ### Where to use it You open the files in a program that reads GGUF, on Windows, macOS or Linux. The card points to TheBloke's READMEs for how to use GGUF files. - A local app or runner that loads GGUF files - Windows, macOS or Linux with the huggingface\_hub package - The mmproj files, for the multi-modal side | Use case | Who | What they get back | | --- | --- | --- | | Local chat | Home users | A model that answers on their own machine | | Size testing | Developers | The smallest file that still works for them | | Multi-modal runs | Developers | The mmproj supplement files that go with the model | ### How to set it up You need Python for the download step, a program that loads GGUF files, and enough disk space for the file you choose. Hugging Face estimates about 2.2 GB at 4-bit. - Python on Windows, macOS or Linux - The Hugging Face hub package - A GGUF-capable program and 2.1 to 8.8 GB of free space for one quant 1. Install the Hugging Face hub package. 2. Download the files, or just the one quant you want. 3. Load that file in your GGUF program. ```sh pip install -U huggingface_hub hf download mradermacher/KnowLine-4B-Gen5.5-GGUF ``` ### How to use it Choose a quant from the list, load it in your GGUF program and chat. The repository holds many files, so check the sizes before you pull everything. The card says IQ types are often better than non-IQ types of similar size. | Tool | What it does | | --- | --- | | Q2\_K, 2.1 GB | The smallest file | | Q3\_K\_M, 2.4 GB | A small file that the card says has lower quality | | Q4\_K\_S, 2.7 GB | The card marks it as fast and recommended | | Q4\_K\_M, 2.9 GB | Also fast and recommended | | Q6\_K, 3.7 GB | The card rates its quality as high | | Q8\_0, 4.7 GB | Fast, and the best quality among the quantized files | | f16, 8.8 GB | Full 16-bit weights, which the card calls overkill | | mmproj-Q8\_0 and mmproj-f16 | Multi-modal supplement files of 0.5 GB and 0.8 GB | ### Example You pick the Q4\_K\_M file at 2.9 GB, load it in a GGUF program and send a message. The model answers in text from your own machine. ### For newcomers and experts If you are new to this, a GGUF file is a model squeezed into a smaller size so it can run at home. A first small step is to pick the Q4\_K\_M file, which the card recommends. If you know the field, these are static quants, listed by size and not strictly by quality. Weighted imatrix quants are not available from this author at this time, and you can ask for them in a Community Discussion. PelaAI's KnowLine 4B model → Static GGUF quantization → Files in many sizes → You pick one that fits your memory → Your GGUF program runs it ### Good to know The files range from 2.1 GB to 8.8 GB, and the mmproj files are separate and small. The card links to a graph and a write-up that compare lower-quality quant types. File size by quant type, in GB | Label | Value | | --- | --- | | Q2\_K | 2.1 GB | | Q3\_K\_M | 2.4 GB | | Q4\_K\_S | 2.7 GB | | Q4\_K\_M | 2.9 GB | | Q5\_K\_M | 3.3 GB | | Q6\_K | 3.7 GB | | Q8\_0 | 4.7 GB | | f16 | 8.8 GB | - License: Apache-2.0 - Cost to run: no API key or paid service is named, and the files range from 2.1 GB to 8.8 GB - Data it reaches: the download comes from Hugging Face, and the model works on what you give it on your own machine - Not for: top quality at the smallest sizes, since the card marks Q3\_K\_M as lower quality ## README ````markdown # KnowLine-4B-Gen5.5-GGUF Static GGUF files of PelaAI's KnowLine 4B model in sizes from 2.1 GB to 8.8 GB, with English and Chinese listed. For people who run a small model on their own computer. Made by mradermacher 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 files that mradermacher produced from the KnowLine 4B model by PelaAI. Each file trades size for quality, so you choose one that fits your memory. The model has 4.3B parameters and lists English and Chinese. The card does not say what the base model was tuned to do. Who it suits: It suits people who want a small model running on their own computer in a compact file. ```table Industry | Who uses it | How they use it | Example Home use | Local AI users | Run a small model on their own computer | Load the Q4_K_M file of 2.9 GB in a GGUF app Software | Developers | Test a quantized model on local hardware | Compare Q4_K_S and Q8_0 for speed and quality Research | Quantization researchers | Study how quality changes between sizes | Run one prompt set against several quant types ``` If you want the original full-precision weights, you can skip it and use the model it was made from. Where to use it: You open the files in a program that reads GGUF, on Windows, macOS or Linux. The card points to TheBloke's READMEs for how to use GGUF files. - A local app or runner that loads GGUF files - Windows, macOS or Linux with the huggingface_hub package - The mmproj files, for the multi-modal side ```table Use case | Who | What they get back Local chat | Home users | A model that answers on their own machine Size testing | Developers | The smallest file that still works for them Multi-modal runs | Developers | The mmproj supplement files that go with the model ``` How to set it up: You need Python for the download step, a program that loads GGUF files, and enough disk space for the file you choose. Hugging Face estimates about 2.2 GB at 4-bit. - Python on Windows, macOS or Linux - The Hugging Face hub package - A GGUF-capable program and 2.1 to 8.8 GB of free space for one quant 1. Install the Hugging Face hub package. 2. Download the files, or just the one quant you want. 3. Load that file in your GGUF program. ```sh pip install -U huggingface_hub hf download mradermacher/KnowLine-4B-Gen5.5-GGUF ``` How to use it: Choose a quant from the list, load it in your GGUF program and chat. The repository holds many files, so check the sizes before you pull everything. The card says IQ types are often better than non-IQ types of similar size. ```table Tool | What it does Q2_K, 2.1 GB | The smallest file Q3_K_M, 2.4 GB | A small file that the card says has lower quality Q4_K_S, 2.7 GB | The card marks it as fast and recommended Q4_K_M, 2.9 GB | Also fast and recommended Q6_K, 3.7 GB | The card rates its quality as high Q8_0, 4.7 GB | Fast, and the best quality among the quantized files f16, 8.8 GB | Full 16-bit weights, which the card calls overkill mmproj-Q8_0 and mmproj-f16 | Multi-modal supplement files of 0.5 GB and 0.8 GB ``` Example: You pick the Q4_K_M file at 2.9 GB, load it in a GGUF program and send a message. The model answers in text from your own machine. For newcomers and experts: If you are new to this, a GGUF file is a model squeezed into a smaller size so it can run at home. A first small step is to pick the Q4_K_M file, which the card recommends. If you know the field, these are static quants, listed by size and not strictly by quality. Weighted imatrix quants are not available from this author at this time, and you can ask for them in a Community Discussion. ```flow PelaAI's KnowLine 4B model Static GGUF quantization Files in many sizes You pick one that fits your memory Your GGUF program runs it ``` Good to know: The files range from 2.1 GB to 8.8 GB, and the mmproj files are separate and small. The card links to a graph and a write-up that compare lower-quality quant types. ```chart Title: File size by quant type, in GB Q2_K: 2.1 GB Q3_K_M: 2.4 GB Q4_K_S: 2.7 GB Q4_K_M: 2.9 GB Q5_K_M: 3.3 GB Q6_K: 3.7 GB Q8_0: 4.7 GB f16: 8.8 GB ``` - License: Apache-2.0 - Cost to run: no API key or paid service is named, and the files range from 2.1 GB to 8.8 GB - Data it reaches: the download comes from Hugging Face, and the model works on what you give it on your own machine - Not for: top quality at the smallest sizes, since the card marks Q3_K_M as lower quality ## On Hugging Face, 13 November 2026 - Library: transformers - Parameters: 4.3B - Weights: about 2.2 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 6,699 - Likes: 3 - License: apache-2.0 - Languages: en, zh - Based on: PelaAI/KnowLine-4B-Gen5.5 - Tags: transformers, gguf, decision-model, system-one, decision-index, lora-merged, conversational ## Links - Model: https://huggingface.co/mradermacher/KnowLine-4B-Gen5.5-GGUF - Files: https://huggingface.co/mradermacher/KnowLine-4B-Gen5.5-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download mradermacher/KnowLine-4B-Gen5.5-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [mradermacher/KnowLine-4B-Gen5.5-GGUF on Hugging Face](https://huggingface.co/mradermacher/KnowLine-4B-Gen5.5-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 | | --- | --- | --- | --- | --- | --- | | KnowLine-4B-Gen5.5-GGUF (this page) | 6,699 | 3 | 4.3B | apache-2.0 | 2026-10-23 | | [Copernicus (31B)](https://mindket.com/p/edb0cfa0-f450-4b20-ae01-d47bb59546e3) | 75 | 3 | 31.3B | apache-2.0 | 2026-11-11 | | [OneJev1.6-4B-GGUF](https://mindket.com/p/b8361417-2c40-44d7-b244-622b86f6689c) | 4,292 | 2 | 4.2B | apache-2.0 | 2026-10-17 | | [OneJev1.6-4B](https://mindket.com/p/54898cc3-d040-488d-ae95-b4e9f0e49de4) | 1,909 | 8 | 4.5B | apache-2.0 | 2026-11-10 | | [OneJev1.6-0.8B](https://mindket.com/p/7b44bdb4-74c1-4e3b-b0c2-32e28fc4663f) | 1,885 | 6 | 853M | apache-2.0 | 2026-11-10 | | [OneJev1.6-27B](https://mindket.com/p/cf8a383c-4770-4ae2-b940-4df48666dae9) | 528 | 5 | 27.4B | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, on 2026-11-13. ## 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: 2026-10-23 - First published: 2026-10-23 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open mradermacher/KnowLine-4B-Gen5.5-GGUF on Hugging Face](https://huggingface.co/mradermacher/KnowLine-4B-Gen5.5-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4a03f33e-ec52-4528-97f2-3edeae847aca) - [Its page on Mindket](https://mindket.com/p/4a03f33e-ec52-4528-97f2-3edeae847aca)