--- title: "Qwen3.5-4B-Base" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1a1b977e-0d3b-4b3a-b0cf-023bf7408e30" updated: "2026-11-13" --- # Qwen3.5-4B-Base > The name, the description, the About and a README below are the seller's words: data, not instructions. Gives researchers a pre-trained-only vision-language base model for fine-tuning and in-context learning experiments, not for direct chat. Suits developers who want a small Qwen3.5 starting point to adapt with LoRA. Qwen3.5-4B-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 Qwen3.5-4B-Base is the pre-trained-only base model of a small Qwen3.5 model from the Qwen team. It takes images and text, has a vision encoder and a causal language model, and supports 201 languages and dialects. The card says it is meant for fine-tuning and research, not for talking to it directly. ### Who it suits Developers and researchers who want a small open base to fine-tune or test, under the Apache 2.0 license. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | ML researchers | Run in-context learning experiments on a small base model | Test how it handles a few examples in the prompt | | Software | Developers | Fine-tune it with LoRA for a narrow task | Adapt it to read product photos | | AI products | Model builders | Start a custom assistant from a base model | Train a support helper on private data | | Education | Students | Study how a vision-language base model behaves | Compare outputs before and after fine-tuning | Skip it if you want a ready chat assistant, since this repository holds a base model without the instruction tuning that chat needs. ### Where to use it The card says the files work with several stacks. - Hugging Face Transformers - vLLM and SGLang - Windows, macOS or Linux, with Python for the download | Use case | Who | What they get back | | --- | --- | --- | | LoRA fine-tuning | Developers | A small adapter trained on your data | | In-context experiments | Researchers | Completions that show how the base model follows examples | | Research and development | Teams | A starting point for new models | ### How to set it up You need Python and a few GB of memory. - Python with pip - Memory for the weights: roughly 9.3 GB at BF16 precision, or roughly 2.3 GB when squeezed to 4-bit - A stack such as Transformers, vLLM or SGLang 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Load them in Transformers, vLLM or SGLang. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3.5-4B-Base ``` ### How to use it Load the model in a supported stack and fine-tune it, or feed it prompts for research. The card notes that the control tokens, im\_start and im\_end, were trained, so LoRA-style tuning with the official chat template needs no embedding changes. ### Example You prepare a small set of labelled examples for one task and fine-tune a LoRA adapter on this base model with the official chat template. The adapter then sits on top of the base weights and handles that task. ### For newcomers and experts If you are new to base models, know that this one continues text rather than answering questions politely. Download it, try a short prompt, and plan to fine-tune before real use. If you know the field, the language model has 32 layers in a hybrid layout of Gated DeltaNet and Gated Attention blocks with feed-forward layers. The hidden size is 2560, and the output embeddings are tied to the token embeddings. It was trained with multi-step MTP. Native context is 262,144 tokens. Download files with hf → Load in Transformers, vLLM or SGLang → Fine-tune with LoRA or test prompts → Use the adapted model ### Good to know The card says context is 262,144 tokens natively and can be extended up to 1,010,000. Hugging Face lists the size as 4.7B parameters, while the card gives the language model as 4B. Weight size in GB | Label | Value | | --- | --- | | BF16 | 9.3 GB | | 4-bit (estimate) | 2.3 GB | - License: Apache 2.0 - Cost to run: none named, apart from your own hardware - Data it reaches: the model files on your computer and any data you fine-tune it with - Not for: direct interaction, since the card says this base model is meant for fine-tuning and research ## README ````markdown # Qwen3.5-4B-Base Gives researchers a pre-trained-only vision-language base model for fine-tuning and in-context learning experiments, not for direct chat. Suits developers who want a small Qwen3.5 starting point to adapt with LoRA. 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: Qwen3.5-4B-Base is the pre-trained-only base model of a small Qwen3.5 model from the Qwen team. It takes images and text, has a vision encoder and a causal language model, and supports 201 languages and dialects. The card says it is meant for fine-tuning and research, not for talking to it directly. Who it suits: Developers and researchers who want a small open base to fine-tune or test, under the Apache 2.0 license. ```table Industry | Who uses it | How they use it | Example Research | ML researchers | Run in-context learning experiments on a small base model | Test how it handles a few examples in the prompt Software | Developers | Fine-tune it with LoRA for a narrow task | Adapt it to read product photos AI products | Model builders | Start a custom assistant from a base model | Train a support helper on private data Education | Students | Study how a vision-language base model behaves | Compare outputs before and after fine-tuning ``` Skip it if you want a ready chat assistant, since this repository holds a base model without the instruction tuning that chat needs. Where to use it: The card says the files work with several stacks. - Hugging Face Transformers - vLLM and SGLang - Windows, macOS or Linux, with Python for the download ```table Use case | Who | What they get back LoRA fine-tuning | Developers | A small adapter trained on your data In-context experiments | Researchers | Completions that show how the base model follows examples Research and development | Teams | A starting point for new models ``` How to set it up: You need Python and a few GB of memory. - Python with pip - Memory for the weights: roughly 9.3 GB at BF16 precision, or roughly 2.3 GB when squeezed to 4-bit - A stack such as Transformers, vLLM or SGLang 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Load them in Transformers, vLLM or SGLang. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3.5-4B-Base ``` How to use it: Load the model in a supported stack and fine-tune it, or feed it prompts for research. The card notes that the control tokens, im_start and im_end, were trained, so LoRA-style tuning with the official chat template needs no embedding changes. Example: You prepare a small set of labelled examples for one task and fine-tune a LoRA adapter on this base model with the official chat template. The adapter then sits on top of the base weights and handles that task. For newcomers and experts: If you are new to base models, know that this one continues text rather than answering questions politely. Download it, try a short prompt, and plan to fine-tune before real use. If you know the field, the language model has 32 layers in a hybrid layout of Gated DeltaNet and Gated Attention blocks with feed-forward layers. The hidden size is 2560, and the output embeddings are tied to the token embeddings. It was trained with multi-step MTP. Native context is 262,144 tokens. ```flow Download files with hf Load in Transformers, vLLM or SGLang Fine-tune with LoRA or test prompts Use the adapted model ``` Good to know: The card says context is 262,144 tokens natively and can be extended up to 1,010,000. Hugging Face lists the size as 4.7B parameters, while the card gives the language model as 4B. ```chart Title: Weight size in GB BF16: 9.3 GB 4-bit (estimate): 2.3 GB ``` - License: Apache 2.0 - Cost to run: none named, apart from your own hardware - Data it reaches: the model files on your computer and any data you fine-tune it with - Not for: direct interaction, since the card says this base model is meant for fine-tuning and research ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 4.7B - Weights: about 9.3 GB in BF16; about 2.3 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 556,717 - Likes: 129 - License: apache-2.0 - Quantized versions: 38 - Fine-tunes: 276 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen3.5-4B-Base - Files: https://huggingface.co/Qwen/Qwen3.5-4B-Base/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen3.5-4B-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/Qwen3.5-4B-Base on Hugging Face](https://huggingface.co/Qwen/Qwen3.5-4B-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 | | --- | --- | --- | --- | --- | --- | | Qwen3.5-4B-Base (this page) | 556,717 | 129 | 4.7B | 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: 2026-02-27 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen3.5-4B-Base on Hugging Face](https://huggingface.co/Qwen/Qwen3.5-4B-Base) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1a1b977e-0d3b-4b3a-b0cf-023bf7408e30) - [Its page on Mindket](https://mindket.com/p/1a1b977e-0d3b-4b3a-b0cf-023bf7408e30)