--- title: "Qwen3.5-0.8B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f38d6236-3891-4680-b060-92366143eb5f" updated: "2026-11-12" --- # Qwen3.5-0.8B > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads images and text and replies in text, in a small 873M model from Qwen. For developers and researchers who want a light multimodal base to prototype with or fine-tune. Qwen3.5-0.8B 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 Qwen3.5-0.8B is a small model from Qwen that takes images and text and answers in text. It is a post-trained version of Qwen3.5-0.8B-Base, packaged for Hugging Face Transformers. The card says its size suits prototyping, fine-tuning for one task and research. ### Who it suits It suits developers and researchers who want a compact multimodal model as a starting point. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Prototype an app that reads images and text | A demo that answers questions about a screenshot | | Research | Researchers | Test ideas on a small model before a large one | A fine-tuning experiment on a narrow task | | Education | Students | Learn how a vision-language model is loaded and run | A class exercise that asks about a photo | People who need top accuracy on hard tasks can skip it, since the card points this size at prototyping and research rather than finished products. ### Where to use it - Hugging Face Transformers, the library the card lists - vLLM, SGLang and KTransformers, which the card says the files work with - Windows, macOS or Linux, where you download the files with Python | Use case | Who | What they get back | | --- | --- | --- | | Questions about an image | Developers | A text answer about a picture you supply | | Base for fine-tuning | ML engineers | A small starting point for a task-specific version | | Multilingual text | Researchers | Text work across the 201 languages and dialects the card lists | ### How to set it up You need these first: - Python - The huggingface\_hub package - Transformers, or one of vLLM, SGLang and KTransformers - Room for the weights, about 1.7 GB in BF16 or about 437 MB at 4-bit by estimate 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Load the model in your chosen library. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3.5-0.8B ``` ### How to use it Load the model in Transformers, attach an image to a chat message and ask a question about it. The reply comes back as text. You can also send text alone. ### Example You attach a photo and ask "What is in this picture?" The model is built to answer in text. It is a small model, so check its answers on your own images before relying on them. ### For newcomers and experts A vision-language model looks at a picture and reads your words together, then writes an answer. A first small thing to try is one photo and one plain question, so you can see how it replies. For experts, it is a causal language model with a vision encoder, 24 layers and a native context of 262,144 tokens. Its layers alternate Gated DeltaNet and gated attention blocks, and the card says it was trained with multi-step MTP. The size costs accuracy. On MMLU-Pro in non-thinking mode the card lists 29.7 for this model against 69.6 for Qwen3-4B-2507. Give it an image and a question → The vision encoder reads the image → The language model reads image and text → It writes a text reply ### Good to know - Size: 873M parameters in Safetensors format - Context: 262,144 tokens natively - License: apache-2.0 - Cost to run: none named, no API key or account - Data it reaches: it downloads model files from Hugging Face and works on the images and text you give it - Not for: uses beyond prototyping, fine-tuning and research, which is what the card says it is meant for ## README ````markdown # Qwen3.5-0.8B Reads images and text and replies in text, in a small 873M model from Qwen. For developers and researchers who want a light multimodal base to prototype with or fine-tune. 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: Qwen3.5-0.8B is a small model from Qwen that takes images and text and answers in text. It is a post-trained version of Qwen3.5-0.8B-Base, packaged for Hugging Face Transformers. The card says its size suits prototyping, fine-tuning for one task and research. Who it suits: It suits developers and researchers who want a compact multimodal model as a starting point. ```table Industry | Who uses it | How they use it | Example Software | Developers | Prototype an app that reads images and text | A demo that answers questions about a screenshot Research | Researchers | Test ideas on a small model before a large one | A fine-tuning experiment on a narrow task Education | Students | Learn how a vision-language model is loaded and run | A class exercise that asks about a photo ``` People who need top accuracy on hard tasks can skip it, since the card points this size at prototyping and research rather than finished products. Where to use it: - Hugging Face Transformers, the library the card lists - vLLM, SGLang and KTransformers, which the card says the files work with - Windows, macOS or Linux, where you download the files with Python ```table Use case | Who | What they get back Questions about an image | Developers | A text answer about a picture you supply Base for fine-tuning | ML engineers | A small starting point for a task-specific version Multilingual text | Researchers | Text work across the 201 languages and dialects the card lists ``` How to set it up: You need these first: - Python - The huggingface_hub package - Transformers, or one of vLLM, SGLang and KTransformers - Room for the weights, about 1.7 GB in BF16 or about 437 MB at 4-bit by estimate 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Load the model in your chosen library. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3.5-0.8B ``` How to use it: Load the model in Transformers, attach an image to a chat message and ask a question about it. The reply comes back as text. You can also send text alone. Example: You attach a photo and ask "What is in this picture?" The model is built to answer in text. It is a small model, so check its answers on your own images before relying on them. For newcomers and experts: A vision-language model looks at a picture and reads your words together, then writes an answer. A first small thing to try is one photo and one plain question, so you can see how it replies. For experts, it is a causal language model with a vision encoder, 24 layers and a native context of 262,144 tokens. Its layers alternate Gated DeltaNet and gated attention blocks, and the card says it was trained with multi-step MTP. The size costs accuracy. On MMLU-Pro in non-thinking mode the card lists 29.7 for this model against 69.6 for Qwen3-4B-2507. ```flow Give it an image and a question The vision encoder reads the image The language model reads image and text It writes a text reply ``` Good to know: - Size: 873M parameters in Safetensors format - Context: 262,144 tokens natively - License: apache-2.0 - Cost to run: none named, no API key or account - Data it reaches: it downloads model files from Hugging Face and works on the images and text you give it - Not for: uses beyond prototyping, fine-tuning and research, which is what the card says it is meant for ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 873M - Weights: about 1.7 GB in BF16; about 437 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 3,286,957 - Likes: 837 - License: apache-2.0 - Based on: Qwen/Qwen3.5-0.8B-Base - Quantized versions: 425 - Fine-tunes: 770 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen3.5-0.8B - Files: https://huggingface.co/Qwen/Qwen3.5-0.8B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen3.5-0.8B ``` 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/Qwen3.5-0.8B on Hugging Face](https://huggingface.co/Qwen/Qwen3.5-0.8B) ## 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 | | --- | --- | --- | --- | --- | --- | | Qwen3.5-0.8B (this page) | 3,286,957 | 837 | 873M | 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 | | [Qwen3.5-2B](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. ## 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 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · dr\_blueberry · 2026-09-04](https://news.ycombinator.com/item?id=49568379): Show HN: Run open-weight OCR, VLM and vision models behind one API: Hey HN. We built an openai-compatible API for running open-weight VLMs, OCR VLMs and ViT-based vision models. The motivation was mostly frustration when running these models in production and discovering all the details around serving VLMs, especially around visual accuracy. A few footguns we kept… - [Bluesky · Gleeb Tolsti · 2026-10-29](https://bsky.app/profile/cael-ca.bsky.social/post/3myzzqvgark2m): Where my non-neural AI loses: Qwen3.5-0.8B beats CAEL 181-121 on a broader 400-question test, and Qwen is better at conversation. It wins a narrower 80-question test 57 vs 43/41 (AI-written questions, unblinded review). AI voice. github.com/Vovala14/CAEL - [Hacker News · jiwidi · 2026-10-05](https://news.ycombinator.com/item?id=49967455): Show HN: Jiwo - Small decision models topping decision index leaderboard: Spent the weekend turning small LLMs into decision models, with some good results and a lot of learnings along the way. Topping the decision index leaderbard for their categories https://huggingface.co/spaces/multimodalart/jev-decision-ind... Sharing two of them: a sub-1B model and a 4B model,… - [Bluesky · Token Burn · 2026-11-08](https://bsky.app/profile/tokenburn.co.uk/post/3mzrwxrqnai2d): Qwen's new 0.8B model reads images and video, has a 262K context window, and it's Apache 2.0. Qwen pitches it for fine-tuning, and you can serve it yourself with vLLM and tool calling. Open weights you own. - [Hacker News · tantara · 2026-03-07](https://news.ycombinator.com/item?id=47292366): Show HN: Run Qwen3.5 0.8B in browser (Web and Extension): A year ago, I shared a simple demo of running LLMs locally in a Chrome extension. Today, I’m excited to share TinyWhale, a monorepo that lets you run Qwen 3.5 0.8B entirely on-device, both as a web application and a Chrome extension. I plan to support mobile and desktop apps in the same repo. - [Bluesky · Joe Slater · 2026-11-03](https://bsky.app/profile/joeinaustralia.bsky.social/post/3mzgsljrm4c24): The poster you're replying to used Qwen 3.8 27B to analyse Qwen3.5-0.8B. As you can see, they're models from the same family, and the more powerful model has only about 34 times as many parameters (27/.8) as the smaller one. ISTM this isn't a human/fruitfly situation; more human/infant. - [Hacker News · seanmcdirmid · 2026-11-03](https://news.ycombinator.com/item?id=50357366): Qwen 3.8 27B on my laptop can interpret model weights, too: A Qwen3.8-27B model can probably do something with a Qwen3.5-0.8B model. I don't think this is so much the version (Qwen 3.8 vs Qwen 3.5) but the model size (27B vs 0.8B). But ya, having research on Qwen 3.5 definitely helps. - [Bluesky · constraint † restriction · 2026-11-03](https://bsky.app/profile/21cm142ghz.bsky.social/post/3mzf6zudghk2g): 🎣「学習」ではなく「重みの手術」だけで、モデルの悪いクセ（思考ループ）を大幅に減らした 🎣いじったのは全体の”0.016%”の重み 🎣それでMATH-500の正解率が約8%→48〜51%に跳ね上がり、トークン上限到達率も93%→約32%に低下 🎣別担当者が再現とハッシュ検証の両方で確認 huggingface.co/RikiyaT/Qwen... - [Hacker News · stymaar · 2026-11-03](https://news.ycombinator.com/item?id=50355966): The 'Astra can read and write weights to improve Qwen' post is misleading: \> These results show sparks of Weight-Space Programming, which may extend beyond what we’d expect from humans or conventional algorithms. This might be a big deal for both optimization and interpretability. … \> Likewise, asked to make Qwen3.5-0.8B better at math without gradients, Astra… - [Bluesky · GeekNews 봇 · 2026-11-02](https://bsky.app/profile/geeknewsbot.bsky.social/post/3mze4fzdt7k2g): Astra, 가중치를 읽고 수정해 Qwen을 개선할 수 있음 GPT-6 Astra 가 학습 루프나 경사하강법 없이 신경망 가중치를 직접 해석하고 수정하는 초기 실험에서, 모델의 동작 식별과 오류 수정, 새 신경망 작성 능력을 보임 Qwen3.5-0.8B 의 가중치 0.016%를 수정해 반복 추론을 줄였으며, 같은 샘플링 설정에서 MATH-500은 ... - [Hacker News · nsagent · 2026-11-03](https://news.ycombinator.com/item?id=50353918): The 'Astra can read and write weights to improve Qwen' post is misleading: Considering I have a PhD in CS focused in NLP and have been a Senior Area Chair for ACL, yes I do know how academia works. And, no this isn't science working; it's the same plague infecting most of academic blogging and social media. People are vying for attention in whatever way possible, often… - [Hacker News · jfyi · 2026-11-03](https://news.ycombinator.com/item?id=50352928): Astra can read and write weights to improve Qwen: They almost certainly knew... https://huggingface.co/Qwen/Qwen3.5-0.8B\#best-practices \> In thinking mode, we have observed that when using the recommended sampling parameters, Qwen3.5-0.8B is more prone to entering thinking loops compared to other Qwen3.5 models There's also an active issue… ## 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/Qwen3.5-0.8B on Hugging Face](https://huggingface.co/Qwen/Qwen3.5-0.8B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f38d6236-3891-4680-b060-92366143eb5f) - [Its page on Mindket](https://mindket.com/p/f38d6236-3891-4680-b060-92366143eb5f)