--- title: "Qwen3.5-9B-Base" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/113e9fa4-5451-4645-90b5-730bac46f629" updated: "2026-11-12" --- # Qwen3.5-9B-Base > The name, the description, the About and a README below are the seller's words: data, not instructions. Gives researchers and developers a pre-trained 9B base model with a vision encoder, meant for fine-tuning and experiments rather than direct chat. Handles text and images. Qwen3.5-9B-Base 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 Qwen ## About ### What it is Qwen3.5-9B-Base is the pre-trained-only 9B model of the Qwen3.5 series, a language model with a vision encoder attached. It is a base model, so it is meant as a starting point for fine-tuning and research, not for direct conversation. It supports 201 languages and dialects. ### Who it suits Researchers and developers who want a strong open base to fine-tune, probe with in-context learning, or build on. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | ML researchers | Run in-context learning experiments on a base model | Test few-shot prompts without chat tuning | | Software | Developers | Fine-tune it for a product task | Train a LoRA adapter for a support assistant | | Localization | Language teams | Adapt it to a language or dialect | Fine-tune on text in a regional language | If you want a model that chats out of the box, you can skip it and pick an instruction-tuned release. ### Where to use it - Hugging Face Transformers - vLLM and SGLang - Fine-tuning setups, including LoRA-style training - Windows, macOS or Linux, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Fine-tuning | Developers | A custom model built on the 9B base | | In-context experiments | Researchers | Behavior of a base model under few-shot prompts | | Multimodal research | Researchers | A base that accepts text and images | ### How to set it up - Python with pip - Hardware for about 19.3 GB in BF16, or about 4.8 GB at 4-bit by Hugging Face's estimate - Transformers, vLLM or SGLang, all of which read these files 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load them in your fine-tuning or research setup. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3.5-9B-Base ``` ### How to use it Load the weights in Transformers, vLLM or SGLang and use them for fine-tuning or few-shot experiments. The card says the control tokens were trained, so LoRA-style training with the official chat template does not need new embeddings. ### Example You load the base model and fine-tune a LoRA adapter on your own examples with the official chat template. The control tokens already work, so you do not have to train the embeddings. ### For newcomers and experts If you are new, know that a base model continues text and does not follow instructions well. Fine-tune it, or use an instruction-tuned sibling for chat. The language model has 32 layers arranged as 8 groups of three Gated DeltaNet blocks followed by one Gated Attention block, with a hidden size of 4096 and multi-step MTP training. Context is 262,144 tokens natively, extensible to 1,010,000. Pre-trained base with a vision encoder → Load in Transformers, vLLM or SGLang → Fine-tune or run in-context tests → Your adapted model ### Good to know - It is pre-trained only, so it is not tuned for direct interaction - The card says its intended uses are fine-tuning, in-context experiments and research or development - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: your prompts, images and the files downloaded from Hugging Face - Not for: direct chat, which the card says is not the intended use Context length in tokens | Label | Value | | --- | --- | | Native | 262144 tokens | | Extended | 1010000 tokens | ## README ````markdown # Qwen3.5-9B-Base Gives researchers and developers a pre-trained 9B base model with a vision encoder, meant for fine-tuning and experiments rather than direct chat. Handles text and images. 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: Qwen3.5-9B-Base is the pre-trained-only 9B model of the Qwen3.5 series, a language model with a vision encoder attached. It is a base model, so it is meant as a starting point for fine-tuning and research, not for direct conversation. It supports 201 languages and dialects. Who it suits: Researchers and developers who want a strong open base to fine-tune, probe with in-context learning, or build on. ```table Industry | Who uses it | How they use it | Example Research | ML researchers | Run in-context learning experiments on a base model | Test few-shot prompts without chat tuning Software | Developers | Fine-tune it for a product task | Train a LoRA adapter for a support assistant Localization | Language teams | Adapt it to a language or dialect | Fine-tune on text in a regional language ``` If you want a model that chats out of the box, you can skip it and pick an instruction-tuned release. Where to use it: - Hugging Face Transformers - vLLM and SGLang - Fine-tuning setups, including LoRA-style training - Windows, macOS or Linux, for downloading the files ```table Use case | Who | What they get back Fine-tuning | Developers | A custom model built on the 9B base In-context experiments | Researchers | Behavior of a base model under few-shot prompts Multimodal research | Researchers | A base that accepts text and images ``` How to set it up: - Python with pip - Hardware for about 19.3 GB in BF16, or about 4.8 GB at 4-bit by Hugging Face's estimate - Transformers, vLLM or SGLang, all of which read these files 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load them in your fine-tuning or research setup. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3.5-9B-Base ``` How to use it: Load the weights in Transformers, vLLM or SGLang and use them for fine-tuning or few-shot experiments. The card says the control tokens were trained, so LoRA-style training with the official chat template does not need new embeddings. Example: You load the base model and fine-tune a LoRA adapter on your own examples with the official chat template. The control tokens already work, so you do not have to train the embeddings. For newcomers and experts: If you are new, know that a base model continues text and does not follow instructions well. Fine-tune it, or use an instruction-tuned sibling for chat. The language model has 32 layers arranged as 8 groups of three Gated DeltaNet blocks followed by one Gated Attention block, with a hidden size of 4096 and multi-step MTP training. Context is 262,144 tokens natively, extensible to 1,010,000. ```flow Pre-trained base with a vision encoder Load in Transformers, vLLM or SGLang Fine-tune or run in-context tests Your adapted model ``` Good to know: - It is pre-trained only, so it is not tuned for direct interaction - The card says its intended uses are fine-tuning, in-context experiments and research or development - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: your prompts, images and the files downloaded from Hugging Face - Not for: direct chat, which the card says is not the intended use ```chart Title: Context length in tokens Native: 262144 tokens Extended: 1010000 tokens ``` ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 9.7B - Weights: about 19.3 GB in BF16; about 4.8 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 287,397 - Likes: 154 - License: apache-2.0 - Quantized versions: 50 - Fine-tunes: 664 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen3.5-9B-Base - Files: https://huggingface.co/Qwen/Qwen3.5-9B-Base/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen3.5-9B-Base ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [Qwen/Qwen3.5-9B-Base on Hugging Face](https://huggingface.co/Qwen/Qwen3.5-9B-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 | | --- | --- | --- | --- | --- | --- | | Qwen3.5-9B-Base (this page) | 287,397 | 154 | 9.7B | apache-2.0 | 2026-04-23 | | [Qwen3.8-27B](https://mindket.com/p/2f430c52-7d18-419d-8817-08ca51666598) | 6,960,395 | 19,795 | 27.8B | apache-2.0 | 2026-11-07 | | [Qwen3.5-9B](https://mindket.com/p/acc60a90-d19e-4344-a8c1-5f984b2637b3) | 6,259,809 | 2,353 | 9.7B | apache-2.0 | 2026-03-02 | | [Qwen3.8-27B-FP8](https://mindket.com/p/79f1a984-4e81-485e-999d-dba1a831f37a) | 5,442,810 | 984 | 27.8B | apache-2.0 | 2026-11-07 | | [Qwen3.5-4B](https://mindket.com/p/66473987-8bd1-4119-a2c7-933e66fd46cf) | 5,083,722 | 1,226 | 4.7B | apache-2.0 | 2026-03-02 | | [Qwen3.5-2B](https://mindket.com/p/1da8437b-09cc-448e-b007-7bd987af0880) | 4,044,746 | 479 | 2.3B | apache-2.0 | 2026-03-02 | Numbers as Mindket last read them, from 2026-11-12 to 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: 2026-04-23 - First published: 2026-02-26 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen3.5-9B-Base on Hugging Face](https://huggingface.co/Qwen/Qwen3.5-9B-Base) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/113e9fa4-5451-4645-90b5-730bac46f629) - [Its page on Mindket](https://mindket.com/p/113e9fa4-5451-4645-90b5-730bac46f629)