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Unsloth's GGUF quantized copy of Qwen3.5-9B, a mid-size model that reads images and text and answers in text, for people running models locally.
Qwen3.5-9B-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.
pip install -U huggingface_hub hf download unsloth/Qwen3.5-9B-GGUF
Qwen3.5-9B-GGUF is a GGUF copy of Qwen3.5-9B, made by Unsloth. The base model reads images and text and replies in text. The record lists 9B parameters, and the 4-bit weights come to about 4.5 GB by its estimate.
The Qwen3.5 family is a causal language model with a vision encoder. It lists support for 201 languages and dialects, and a context length of 262,144 tokens natively.
It suits people who want a capable multimodal model on their own computer, without sending files to a hosted service. A file of about 4.5 GB needs a machine with some free memory.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| Software | Developers | Run a local model for text and image questions | Asking what a screenshot shows |
| Education | Students and teachers | Practice and explain material with an offline model | Explaining a diagram from a textbook |
| Office work | Analysts | Summarize documents and read charts locally | Describing a chart image in words |
| Research | Hobbyist researchers | Try a recent model on their own hardware | Comparing answers across languages |
Skip it if you want a hosted chat service and do not run models yourself.
| Use case | Who | What they get back |
|---|---|---|
| Ask about an image | Anyone with a local setup | A written answer about the picture |
| Long document questions | Researchers | Answers from a context window of 262,144 tokens |
| Multilingual chat | Global teams | Replies in many of the 201 languages the card lists |
You need a few things in place first.
pip install -U huggingface_hub hf download unsloth/Qwen3.5-9B-GGUF
Once the file is loaded in your local app, you chat with it or send it images with a question. Training your own variant on your machine is also possible through Unsloth, the maker of this copy.
You load the file and attach a photo of a chart with the question "What does this chart show?" The model answers in text. It describes what the picture contains and what the question needs.
Start small. Download the file, open it in a GGUF-capable app and ask one plain question before you try images. If it runs well, add a picture.
Qwen3.5 mixes Gated DeltaNet layers with gated attention layers, in a pattern repeated 8 times across 32 layers. The card calls this a hybrid design that keeps inference fast. The GGUF copy trades some precision for a much smaller file. Unsloth says its Dynamic 2.0 quant method keeps accuracy high, and the card gives a native context of 262,144 tokens that can stretch to 1,010,000.
Unsloth's GGUF quantized copy of Qwen3.5-9B, a mid-size model that reads images and text and answers in text, for people running models locally.
Made by unsloth 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.
What it is: Qwen3.5-9B-GGUF is a GGUF copy of Qwen3.5-9B, made by Unsloth. The base model reads images and text and replies in text. The record lists 9B parameters, and the 4-bit weights come to about 4.5 GB by its estimate.
The Qwen3.5 family is a causal language model with a vision encoder. It lists support for 201 languages and dialects, and a context length of 262,144 tokens natively.
Who it suits: It suits people who want a capable multimodal model on their own computer, without sending files to a hosted service. A file of about 4.5 GB needs a machine with some free memory.
Industry | Who uses it | How they use it | Example
Software | Developers | Run a local model for text and image questions | Asking what a screenshot shows
Education | Students and teachers | Practice and explain material with an offline model | Explaining a diagram from a textbook
Office work | Analysts | Summarize documents and read charts locally | Describing a chart image in words
Research | Hobbyist researchers | Try a recent model on their own hardware | Comparing answers across languages
Skip it if you want a hosted chat service and do not run models yourself.
Where to use it:
Use case | Who | What they get back
Ask about an image | Anyone with a local setup | A written answer about the picture
Long document questions | Researchers | Answers from a context window of 262,144 tokens
Multilingual chat | Global teams | Replies in many of the 201 languages the card lists
How to set it up: You need a few things in place first.
pip install -U huggingface_hub
hf download unsloth/Qwen3.5-9B-GGUF
How to use it: Once the file is loaded in your local app, you chat with it or send it images with a question. Training your own variant on your machine is also possible through Unsloth, the maker of this copy.
Example: You load the file and attach a photo of a chart with the question "What does this chart show?" The model answers in text. It describes what the picture contains and what the question needs.
For newcomers and experts: Start small. Download the file, open it in a GGUF-capable app and ask one plain question before you try images. If it runs well, add a picture.
Qwen3.5 mixes Gated DeltaNet layers with gated attention layers, in a pattern repeated 8 times across 32 layers. The card calls this a hybrid design that keeps inference fast. The GGUF copy trades some precision for a much smaller file. Unsloth says its Dynamic 2.0 quant method keeps accuracy high, and the card gives a native context of 262,144 tokens that can stretch to 1,010,000.
Download the GGUF file with hf
Load it in a local app
Send text or an image
The model replies in text
Good to know:
Download its files with Hugging Face's command line:
pip install -U huggingface_hub
hf download unsloth/Qwen3.5-9B-GGUF
Then follow the usage in its model card. Nothing in this kit runs by itself.
README.md 4 KB
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-GGUF This page | 1,969,901 | 1,074 | 9B | apache-2.0 | |
| Qwen3.6-35B-A3B-MTP-GGUFShares unsloth · image-text-to-text · gguf | 2,315,167 | 1,051 | 35.5B | apache-2.0 | |
| Qwen3.6-35B-A3B-GGUFShares unsloth · image-text-to-text · gguf | 1,649,871 | 1,789 | 34.7B | apache-2.0 | |
| Qwen3.5-4B-GGUFShares unsloth · image-text-to-text · gguf | 1,537,690 | 557 | 4.2B | apache-2.0 | |
| Qwopus3.8-27B-Flash-V2-GGUFShares unsloth · image-text-to-text · gguf | 897,675 | 75 | 461M | apache-2.0 | |
| Muse Glimmer 30B GGUFShares unsloth · image-text-to-text · gguf | 692,334 | 592 | 27.9B | apache-2.0 |
Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search and on Trendshift. Mindket doesn’t write or check them. Open one to read it where it was posted.
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