--- title: "Qwen3-1.7B-Base" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e1759baf-ffc9-43e2-b76e-42fff0c1d597" updated: "2026-11-13" --- # Qwen3-1.7B-Base > The name, the description, the About and a README below are the seller's words: data, not instructions. Continues text and code as a 1.7B base language model pre-trained on 119 languages. For developers who want a small model to fine-tune or test. Qwen3-1.7B-Base 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 Qwen ## About ### What it is Qwen3-1.7B-Base is a small base language model from the Qwen3 family. It has 1.7 billion parameters and sits at the pretraining stage, so it continues text rather than following chat instructions. Its pretraining covered 36 trillion tokens in 119 languages. ### Who it suits It suits developers and researchers who want a compact base model to fine-tune, quantize or test ideas on. Hugging Face lists 56 quantized and 504 fine-tuned versions made by other people. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI research | ML researchers | Test training ideas on a small base model | Comparing two fine-tuning recipes on the same starting point | | Software development | Application developers | Fine-tune it for a narrow text task | Adapting it to complete text in one product | | Model distribution | Packagers | Make smaller quantized copies | Shrinking the weights from BF16 to 4-bit | You can skip it when you need a model that follows instructions out of the box, since this is the base version. ### Where to use it - Python with the transformers library - Windows, macOS or Linux, once the files are downloaded - Your own fine-tuning and quantizing workflows | Use case | Who | What they get back | | --- | --- | --- | | Text continuation | Developers | Text that carries on from the words you give it | | Fine-tuning base | ML engineers | A starting point to train for one task | | Quantized copies | Packagers | A smaller version for lighter hardware | ### How to set it up - Python on Windows, macOS or Linux - The huggingface\_hub package, to download the files - The latest transformers version, since older ones fail on this model - Room for about 3.4 GB of BF16 weights 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Keep transformers up to date. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-1.7B-Base ``` With a transformers version older than 4.51.0, loading stops with this error. ```text KeyError: 'qwen3' ``` ### How to use it Day to day you load the model with transformers and give it some text to continue. The card shows no chat prompt or code sample, and points to its blog and documentation for more. | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | | transformers | Loads the model, in its latest version | ### Example You give the model the first sentences of a paragraph. It carries on writing from there as plain text completion. It does not answer you as a chat assistant would, because it is a base model. ### For newcomers and experts If you are new, download the files with the two commands above and read the card. Then load the model with transformers and give it a half-written sentence to finish. If you know the field, this is a causal language model with 28 layers, 1.4 billion non-embedding parameters and grouped query attention with 16 query heads and 8 key-value heads. Training ran in three stages, from broad language modeling to reasoning and then longer sequences. Context length is 32,768. You give it the start of some text → The model reads it through 28 layers → It predicts the next tokens → The text continues ### Good to know Attention heads | Label | Value | | --- | --- | | Query heads | 16 heads | | Key-value heads | 8 heads | - Base model: the card describes the pretraining stage only - Context: 32,768 tokens - Size: about 3.4 GB in BF16, or about 860 MB at 4-bit as an estimate - License: apache-2.0 - Cost to run: none named, it runs on your own hardware - Data it reaches: the text you give it, and the model files you download from Hugging Face - Not for: following chat instructions, since the card covers only the pretrained base ## README ````markdown # Qwen3-1.7B-Base Continues text and code as a 1.7B base language model pre-trained on 119 languages. For developers who want a small model to fine-tune or test. Made by Qwen 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: Qwen3-1.7B-Base is a small base language model from the Qwen3 family. It has 1.7 billion parameters and sits at the pretraining stage, so it continues text rather than following chat instructions. Its pretraining covered 36 trillion tokens in 119 languages. Who it suits: It suits developers and researchers who want a compact base model to fine-tune, quantize or test ideas on. Hugging Face lists 56 quantized and 504 fine-tuned versions made by other people. ```table Industry | Who uses it | How they use it | Example AI research | ML researchers | Test training ideas on a small base model | Comparing two fine-tuning recipes on the same starting point Software development | Application developers | Fine-tune it for a narrow text task | Adapting it to complete text in one product Model distribution | Packagers | Make smaller quantized copies | Shrinking the weights from BF16 to 4-bit ``` You can skip it when you need a model that follows instructions out of the box, since this is the base version. Where to use it: - Python with the transformers library - Windows, macOS or Linux, once the files are downloaded - Your own fine-tuning and quantizing workflows ```table Use case | Who | What they get back Text continuation | Developers | Text that carries on from the words you give it Fine-tuning base | ML engineers | A starting point to train for one task Quantized copies | Packagers | A smaller version for lighter hardware ``` How to set it up: - Python on Windows, macOS or Linux - The huggingface_hub package, to download the files - The latest transformers version, since older ones fail on this model - Room for about 3.4 GB of BF16 weights 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Keep transformers up to date. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-1.7B-Base ``` With a transformers version older than 4.51.0, loading stops with this error. ```text KeyError: 'qwen3' ``` How to use it: Day to day you load the model with transformers and give it some text to continue. The card shows no chat prompt or code sample, and points to its blog and documentation for more. ```table Tool | What it does hf download | Fetches the model files to your computer transformers | Loads the model, in its latest version ``` Example: You give the model the first sentences of a paragraph. It carries on writing from there as plain text completion. It does not answer you as a chat assistant would, because it is a base model. For newcomers and experts: If you are new, download the files with the two commands above and read the card. Then load the model with transformers and give it a half-written sentence to finish. If you know the field, this is a causal language model with 28 layers, 1.4 billion non-embedding parameters and grouped query attention with 16 query heads and 8 key-value heads. Training ran in three stages, from broad language modeling to reasoning and then longer sequences. Context length is 32,768. ```flow You give it the start of some text The model reads it through 28 layers It predicts the next tokens The text continues ``` Good to know: ```chart Title: Attention heads Query heads: 16 heads Key-value heads: 8 heads ``` - Base model: the card describes the pretraining stage only - Context: 32,768 tokens - Size: about 3.4 GB in BF16, or about 860 MB at 4-bit as an estimate - License: apache-2.0 - Cost to run: none named, it runs on your own hardware - Data it reaches: the text you give it, and the model files you download from Hugging Face - Not for: following chat instructions, since the card covers only the pretrained base ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 1.7B - Weights: about 3.4 GB in BF16; about 860 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 599,054 - Likes: 101 - License: apache-2.0 - Quantized versions: 56 - Fine-tunes: 504 - Tags: transformers, safetensors, qwen3, text-generation, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen3-1.7B-Base - Files: https://huggingface.co/Qwen/Qwen3-1.7B-Base/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-1.7B-Base ``` 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 [Qwen/Qwen3-1.7B-Base on Hugging Face](https://huggingface.co/Qwen/Qwen3-1.7B-Base) ## 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 | | --- | --- | --- | --- | --- | --- | | Qwen3-1.7B-Base (this page) | 599,054 | 101 | 1.7B | apache-2.0 | 2025-07-26 | | [Qwen3-0.6B](https://mindket.com/p/0598bfe0-640a-4bf0-b9f4-ca4cf70a04ae) | 19,718,599 | 1,894 | 752M | apache-2.0 | 2025-07-26 | | [Qwen3-14B](https://mindket.com/p/56074bfc-f2ce-4109-8314-1af039530db4) | 12,965,376 | 540 | 14.8B | apache-2.0 | 2025-07-26 | | [Qwen3-8B](https://mindket.com/p/408d17f8-b1a4-4b5f-8440-2d40ec001638) | 6,269,141 | 2,184 | 8.2B | apache-2.0 | 2025-07-26 | | [Qwen3-1.7B](https://mindket.com/p/c3175d9e-0ec9-4f84-b948-86f012d7e396) | 3,203,490 | 618 | 2B | apache-2.0 | 2025-07-26 | | [Qwen3-4B](https://mindket.com/p/03e0f350-7bff-440a-963a-2fc01d03afce) | 3,186,531 | 798 | 4B | apache-2.0 | 2025-07-26 | 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. - [Hacker News · bayshark · 2026-06-17](https://news.ycombinator.com/item?id=48576208): Show HN: Selora – local model for Home Assistant: Selora AI Local is an open-source, Qwen-based model for Home Assistant. Specs: Qwen3 1.7B base model (Q6 quantized\~1.6GB) Four Home Assistant-specific LoRA adapters: - Answers - Clarifications - Automations - Commands \~3.5 GB total download size Runs locally via llama.cpp We chose a Qwen-based… - [Bluesky · LLMs · 2026-06-17](https://bsky.app/profile/llms.activitypub.awakari.com.ap.brid.gy/post/3moj6t5wepzc2): Show HN: Selora – local model for Home Assistant Selora AI Local is an open-source, Qwen-based model for Home Assistant. Specs: Qwen3 1.7B base model (Q6 quantized\~1.6GB) Four Home Assistant-spec... Origin \| Interest \| Match - [Bluesky · @aichina.news · 2026-06-15](https://bsky.app/profile/aichina.news/post/3mod44hy2ws2j): Qwen3-1.7B-Base SAEs boast 32,768 steerable features across 28 layers, a clear advance for mechanistic interpretability. But VRAM overhead, manual labelling, and the restrictive Qwen licence hinder practical utility. Hard to see this becoming a production debugging staple until automated feature... ## 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: 2025-07-26 - First published: 2025-04-28 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen3-1.7B-Base on Hugging Face](https://huggingface.co/Qwen/Qwen3-1.7B-Base) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e1759baf-ffc9-43e2-b76e-42fff0c1d597) - [Its page on Mindket](https://mindket.com/p/e1759baf-ffc9-43e2-b76e-42fff0c1d597)