--- title: "Qwen3.5-2B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1da8437b-09cc-448e-b007-7bd987af0880" updated: "2026-11-12" --- # Qwen3.5-2B > The name, the description, the About and a README below are the seller's words: data, not instructions. A small 2B Qwen model that reads text and images and replies in text, for developers who prototype, fine-tune or run research with the transformers library. Qwen3.5-2B 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 Qwen3.5-2B is a small post-trained model from Qwen with about 2.3B parameters by Hugging Face's count. It reads text and images and replies in text. It is built from Qwen3.5-2B-Base and comes as Safetensors files for the transformers library. The card also names vLLM, SGLang and KTransformers as compatible. The card points it at quick prototypes, tuning for a single task and general research. ### Who it suits It suits developers, researchers and machine learning engineers who want a small multimodal model to test ideas on or to adapt to a task of their own. Who uses this model | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Prototype features that read pictures and text | Try a small model before moving to a larger one | | AI research | Researchers | Study a compact multimodal model | Compare it with the other Qwen3.5 sizes | | Machine learning | Fine-tuners | Start a custom model from a small checkpoint | Train it further on a task of their own | If you need the best accuracy on hard tasks, you can skip it, since the card points it at prototyping and research. ### Where to use it - Python scripts that use the transformers library - Servers such as vLLM, SGLang or KTransformers, which the card says work with it - Windows, macOS or Linux with Python, for downloading the files Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Picture and text questions | Developers | A written answer about an image you supply | | Prototyping | Product teams | A quick test of an idea on a small model | | Fine-tuning | Machine learning engineers | A starting point for a model built for one task | | Multilingual text | Global teams | Coverage of 201 languages and dialects, per the card | ### How to set it up You need Python and the huggingface\_hub package. The weights are about 4.5 GB in BF16, or about 1.1 GB at 4-bit by Hugging Face's estimate, so plan your disk and memory around that. - Python with pip - About 4.5 GB of free disk space for the BF16 files - The transformers library, or one of the servers named above 1. Install huggingface\_hub. 2. Download the model files with the hf command. 3. Load them with your chosen library, following the full model card. ```bash pip install -U huggingface_hub hf download Qwen/Qwen3.5-2B ``` ### How to use it The part of the card visible here shows no run steps, so check the full card for the exact loading code. The general pattern for an image text to text model is to load it, pass a picture with a question and read the text reply. Main commands | Tool | What it does | | --- | --- | | pip install -U huggingface\_hub | Installs the helper that downloads model files | | hf download Qwen/Qwen3.5-2B | Saves the model files on your computer | ### Example You download the files, load the model, give it a photo and ask what is in it. A typical reply is a short written description of the picture. ### For newcomers and experts A small model like this is cheap to try. A first small test is to download it and ask one question about a picture you own. The language model has 24 layers and a hidden size of 2048. Its layout repeats six times, with three gated delta layers followed by one gated attention layer, and a vision encoder sits in front. It handles 262,144 tokens of context natively. Picture and text prompt → Vision encoder reads the image → Hybrid language model layers → Text reply ### Good to know In the card's non-thinking MMLU-Pro row, this model scores 55.3, against 69.6 for Qwen3-4B-2507 and 29.7 for Qwen3.5-0.8B. Other people have published 318 quantized and 684 fine-tuned versions. Estimated weight size | Label | Value | | --- | --- | | BF16 | 4.5 GB | | 4-bit | 1.1 GB | - License: Apache 2.0. - Cost to run: none named. - Data it reaches: the files come from Hugging Face, and the model then runs on your own computer. - Not for: relying on it for hard tasks without checking, since the card describes it as a prototyping and research model. ## README ````markdown # Qwen3.5-2B A small 2B Qwen model that reads text and images and replies in text, for developers who prototype, fine-tune or run research with the transformers library. 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: Qwen3.5-2B is a small post-trained model from Qwen with about 2.3B parameters by Hugging Face's count. It reads text and images and replies in text. It is built from Qwen3.5-2B-Base and comes as Safetensors files for the transformers library. The card also names vLLM, SGLang and KTransformers as compatible. The card points it at quick prototypes, tuning for a single task and general research. Who it suits: It suits developers, researchers and machine learning engineers who want a small multimodal model to test ideas on or to adapt to a task of their own. ```table Title: Who uses this model Industry | Who uses it | How they use it | Example Software | Developers | Prototype features that read pictures and text | Try a small model before moving to a larger one AI research | Researchers | Study a compact multimodal model | Compare it with the other Qwen3.5 sizes Machine learning | Fine-tuners | Start a custom model from a small checkpoint | Train it further on a task of their own ``` If you need the best accuracy on hard tasks, you can skip it, since the card points it at prototyping and research. Where to use it: - Python scripts that use the transformers library - Servers such as vLLM, SGLang or KTransformers, which the card says work with it - Windows, macOS or Linux with Python, for downloading the files ```table Title: Jobs it does Use case | Who | What they get back Picture and text questions | Developers | A written answer about an image you supply Prototyping | Product teams | A quick test of an idea on a small model Fine-tuning | Machine learning engineers | A starting point for a model built for one task Multilingual text | Global teams | Coverage of 201 languages and dialects, per the card ``` How to set it up: You need Python and the huggingface_hub package. The weights are about 4.5 GB in BF16, or about 1.1 GB at 4-bit by Hugging Face's estimate, so plan your disk and memory around that. - Python with pip - About 4.5 GB of free disk space for the BF16 files - The transformers library, or one of the servers named above 1. Install huggingface_hub. 2. Download the model files with the hf command. 3. Load them with your chosen library, following the full model card. ```bash pip install -U huggingface_hub hf download Qwen/Qwen3.5-2B ``` How to use it: The part of the card visible here shows no run steps, so check the full card for the exact loading code. The general pattern for an image text to text model is to load it, pass a picture with a question and read the text reply. ```table Title: Main commands Tool | What it does pip install -U huggingface_hub | Installs the helper that downloads model files hf download Qwen/Qwen3.5-2B | Saves the model files on your computer ``` Example: You download the files, load the model, give it a photo and ask what is in it. A typical reply is a short written description of the picture. For newcomers and experts: A small model like this is cheap to try. A first small test is to download it and ask one question about a picture you own. The language model has 24 layers and a hidden size of 2048. Its layout repeats six times, with three gated delta layers followed by one gated attention layer, and a vision encoder sits in front. It handles 262,144 tokens of context natively. ```flow Picture and text prompt Vision encoder reads the image Hybrid language model layers Text reply ``` Good to know: In the card's non-thinking MMLU-Pro row, this model scores 55.3, against 69.6 for Qwen3-4B-2507 and 29.7 for Qwen3.5-0.8B. Other people have published 318 quantized and 684 fine-tuned versions. ```chart Title: Estimated weight size BF16: 4.5 GB 4-bit: 1.1 GB ``` - License: Apache 2.0. - Cost to run: none named. - Data it reaches: the files come from Hugging Face, and the model then runs on your own computer. - Not for: relying on it for hard tasks without checking, since the card describes it as a prototyping and research model. ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 2.3B - Weights: about 4.5 GB in BF16; about 1.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 4,044,746 - Likes: 479 - License: apache-2.0 - Based on: Qwen/Qwen3.5-2B-Base - Quantized versions: 323 - Fine-tunes: 698 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen3.5-2B - Files: https://huggingface.co/Qwen/Qwen3.5-2B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen3.5-2B ``` 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/Qwen3.5-2B on Hugging Face](https://huggingface.co/Qwen/Qwen3.5-2B) ## 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 | | --- | --- | --- | --- | --- | --- | | Qwen3.5-2B (this page) | 4,044,746 | 479 | 2.3B | apache-2.0 | 2026-03-02 | | [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 | | [Qwen3.5-0.8B](https://mindket.com/p/f38d6236-3891-4680-b060-92366143eb5f) | 3,286,957 | 837 | 873M | apache-2.0 | 2026-03-02 | 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 · computerex · 2026-03-07](https://news.ycombinator.com/item?id=47283136): Show HN: I wrote an LLM inference engine in pure Go – 48 tok/s zero dependencies: dlgo is a pure Go deep learning inference engine. It loads GGUF models and runs them on CPU with no dependencies beyond the standard library (SIMD acceleration is optional via CGo). I built this because I wanted to add local LLM inference to a Go project without shelling out to Python or linking… - [Bluesky · @aichina.news · 2026-10-20](https://bsky.app/profile/aichina.news/post/3mycg2bzzv22u): Metadata-only card, no precision specs, no benchmarks, an 'amd' namespace nobody explains. The pull: Apache 2.0 Alibaba weights, packaged for Ascend. Hard to see it beating a self-quantised upstream Qwen until Modelers names the silicon it targets. \*(248 graphemes — same three beats, qualifier... - [Hacker News · yjftsjthsd-h · 2026-11-12](https://news.ycombinator.com/item?id=50473269): I'm making LLMs work on small and old devices: I'm told that qwen 3.8 27B is good enough for practical programming work, so if that's doable then I could easily see the utility. Or... the biggest qwen I see here is Qwen3.5-2B; is that actually capable of programming or agentic work with code? - [Bluesky · Dasari Ranga Eswar · 2026-10-20](https://bsky.app/profile/eswaroy.bsky.social/post/3mybtmid2a223): Can a small language model get better at university physics with nothing but free Colab GPUs? I set out to find out. The result: a fine-tuned Qwen3.5-2B that scores 18.9% vs 15.9% for the original on 1,142 UGPhysics questions it never saw (+3.0 points). 💻 GitHub: github.com/eswaroy/qwen... - [Hacker News · Oras · 2026-09-28](https://news.ycombinator.com/item?id=49885809): Jeff – Jev-compatible 0.8B decision models, trained at home, \~30 ms: My use case is simple classification for job ads. Things like, industry, work settings (remote, hybrid, onsite) and job type (full time, part time .. etc). I did side by side comparison with Gemini 2.5 Flash Lite, Jev, Jeff I tried the 0.8B model, completely useless in classification. Qwen… - [Bluesky · Pingoru.io · 2026-10-20](https://bsky.app/profile/pingoru.bsky.social/post/3mybvfybfxv24): 🟠 Berget AI is reporting a Partial Outage since 05:18 UTC "Qwen/Qwen3.5-2B is down" Affects: Qwen/Qwen3.5-2B Live timeline → https://pingoru.io/providers/berget-ai/incidents/12308884 \#BergetAI \#BergetAIDown - [Hacker News · lostmsu · 2026-08-11](https://news.ycombinator.com/item?id=49254399): LFM2.5 2.6B model competitive with 4x larger models: It's not even competitive with 2x sized Qwen 4B. Why is Qwen3.5 2B not in the table? - [Bluesky · Pingoru.io · 2026-10-16](https://bsky.app/profile/pingoru.bsky.social/post/3mxzjjsxoxv2i): 🟠 Berget AI is reporting a Partial Outage since 21:23 UTC "Qwen/Qwen3.5-2B is down" Affects: Qwen/Qwen3.5-2B Live timeline → https://pingoru.io/providers/berget-ai/incidents/12305341 \#BergetAI \#BergetAIDown - [Hacker News · Farmadupe · 2026-05-11](https://news.ycombinator.com/item?id=48093445): Local AI needs to be the norm: The latest rounds of open weights vision language models are incredibly good. Like, massively good. Open weights vision capabilities trade blows with frontier models. Over the last few months I'd roughly rank capabilities as Gemini -\> {chatgpt and SoTa open weights models} -\> Claude. qwen3.5-2b… - [Bluesky · K. Azwan · 2026-10-16](https://bsky.app/profile/kazwan.bsky.social/post/3mxz35t3yao26): FLock.io Launches Qwen-powered, Open-Source Decision Model for Local Edge Deployment Built on Qwen3.5-2B, FLock.io and Oxford University researchers have launched an open-source AI decision model that outperforms Jev and larger LLMs in various benchmarking tests and can be deployed on local… - [Hacker News · NitpickLawyer · 2026-03-25](https://news.ycombinator.com/item?id=47517210): Ensu – Ente’s Local LLM app: You can see what it uses here - https://github.com/ente-io/ente/blob/main/web/apps/ensu/src/... Either LFM2.5-1.6B-4bit or Qwen3.5-2B-8bit or Qwen3.5-4B-4bit - [Bluesky · Hacker \& Security News · 2026-10-15](https://bsky.app/profile/hacker.at.thenote.app/post/3mxvx6i4pbc24): FLock.io Unveils Edge Decision Model, Built on Qwen, for Enhanced Data Privacy Built on Qwen3.5-2B, the System One model executes sub-31ms local inference, allowing enterprises to run automated classifications quickly, at low cost and completely offline without transmitting sensi… \#hackernews \#news ## 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: 2026-03-02 - First published: 2026-02-28 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen3.5-2B on Hugging Face](https://huggingface.co/Qwen/Qwen3.5-2B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1da8437b-09cc-448e-b007-7bd987af0880) - [Its page on Mindket](https://mindket.com/p/1da8437b-09cc-448e-b007-7bd987af0880)