Huashu Design
alchaincyf · Skill
Turns a one-line request in your AI agent into a finished design: a launch animation, a clickable app prototype, an editable slide deck, an infographic or a se…
8-bit MLX conversion of Qwen3.6-35B-A3B that reads text and images and answers in text, run with the mlx-vlm tool. For developers who want to run a large vision-language model locally.
Qwen3.6-35B-A3B-8bit (MLX) 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 mlx-vlm
This is an 8-bit copy of Qwen3.6-35B-A3B in MLX format, which reads both text and images and answers in text. It was converted with mlx-vlm version 0.4.4. The model has 35.1 billion parameters. For details about the model itself, the card sends you to the original Qwen3.6-35B-A3B card.
It suits developers and tinkerers who want to run a large vision-language model through the MLX toolchain on their own computer.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| Software | App developers | Add image description to a local app | Describing screenshots from a script |
| Research | Model testers | Try an 8-bit conversion of a large model | Comparing replies with another quantization |
| Accessibility | Tool makers | Generate text descriptions of pictures | Writing alt text for a photo |
You can skip it if you do not use MLX, or if you want the original full-precision model.
It runs with the mlx-vlm command line tool and Python, on computers that support MLX, which is usually Apple hardware.
| Use case | Who | What they get back |
|---|---|---|
| Image description | Developers | A text description of a picture you pass in |
| Visual question answering | Researchers | Written answers about an image |
| Local chat with images | Hobbyists | Replies produced entirely on your computer |
You need Python and the mlx-vlm package. Hugging Face lists the files at about 140 GB, so check free disk space first. The model downloads by its name the first time you run the command.
pip install -U mlx-vlm
Run the generate module with the model name, a token limit, a temperature, a prompt and the path to an image. Setting the temperature to 0.0 gives the same reply each time.
python -m mlx_vlm.generate --model mlx-community/Qwen3.6-35B-A3B-8bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
| Tool | What it does |
|---|---|
| mlx_vlm.generate | Runs the model on a prompt and an image |
| --max-tokens | Limits the length of the reply |
| --temperature | Controls how random the reply is, 0.0 for steady output |
| --image | Gives the path to the picture to describe |
You run the command with the prompt "Describe this image." and the path to a photo. The model prints a short text description of what the picture shows, up to 100 tokens long.
If you are new, install mlx-vlm, pick a photo and run the one command above. Read the printed description to see what the model noticed.
If you know the field, this is a quantized conversion for MLX of a mixture-of-experts model from the Qwen3.6 series. The card adds no benchmark data of its own, so refer to the original model's card for capabilities and limits.
The card is short and relies on the original Qwen3.6-35B-A3B card for everything else.
8-bit MLX conversion of Qwen3.6-35B-A3B that reads text and images and answers in text, run with the mlx-vlm tool. For developers who want to run a large vision-language model locally.
Made by mlx-community 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: This is an 8-bit copy of Qwen3.6-35B-A3B in MLX format, which reads both text and images and answers in text. It was converted with mlx-vlm version 0.4.4. The model has 35.1 billion parameters. For details about the model itself, the card sends you to the original Qwen3.6-35B-A3B card.
Who it suits: It suits developers and tinkerers who want to run a large vision-language model through the MLX toolchain on their own computer.
Industry | Who uses it | How they use it | Example
Software | App developers | Add image description to a local app | Describing screenshots from a script
Research | Model testers | Try an 8-bit conversion of a large model | Comparing replies with another quantization
Accessibility | Tool makers | Generate text descriptions of pictures | Writing alt text for a photo
You can skip it if you do not use MLX, or if you want the original full-precision model.
Where to use it: It runs with the mlx-vlm command line tool and Python, on computers that support MLX, which is usually Apple hardware.
Use case | Who | What they get back
Image description | Developers | A text description of a picture you pass in
Visual question answering | Researchers | Written answers about an image
Local chat with images | Hobbyists | Replies produced entirely on your computer
How to set it up: You need Python and the mlx-vlm package. Hugging Face lists the files at about 140 GB, so check free disk space first. The model downloads by its name the first time you run the command.
pip install -U mlx-vlm
How to use it: Run the generate module with the model name, a token limit, a temperature, a prompt and the path to an image. Setting the temperature to 0.0 gives the same reply each time.
python -m mlx_vlm.generate --model mlx-community/Qwen3.6-35B-A3B-8bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
Tool | What it does
mlx_vlm.generate | Runs the model on a prompt and an image
--max-tokens | Limits the length of the reply
--temperature | Controls how random the reply is, 0.0 for steady output
--image | Gives the path to the picture to describe
Example: You run the command with the prompt "Describe this image." and the path to a photo. The model prints a short text description of what the picture shows, up to 100 tokens long.
For newcomers and experts: If you are new, install mlx-vlm, pick a photo and run the one command above. Read the printed description to see what the model noticed.
If you know the field, this is a quantized conversion for MLX of a mixture-of-experts model from the Qwen3.6 series. The card adds no benchmark data of its own, so refer to the original model's card for capabilities and limits.
Photo and a text prompt
mlx-vlm loads the 8-bit model
Model reads the image and prompt
Text reply printed in the terminal
Good to know: The card is short and relies on the original Qwen3.6-35B-A3B card for everything else.
Download its files with Hugging Face's command line:
pip install -U huggingface_hub
hf download mlx-community/Qwen3.6-35B-A3B-8bit
Then follow the usage in its model card. Nothing in this kit runs by itself.
README.md 5 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 |
|---|---|---|---|---|---|
| Qwen3.6-35B-A3B-8bit (MLX) This page | 5,687 | 37 | 35.1B | apache-2.0 | |
| Ornith-1.5-35B-A3B-NVFP4Shares qwen3_5_moe · 8-bit · image-text-to-text | 1,124,337 | 73 | 19.5B | mit | |
| Qwen3.6-35B-A3B-NVFP4-FastShares qwen3_5_moe · 8-bit · image-text-to-text | 695,020 | 134 | 19.8B | apache-2.0 | |
| Ornith-1.5-122B-NVFP4Shares qwen3_5_moe · 8-bit · image-text-to-text | 279,663 | 36 | 64B | mit | |
| Qwen3.6-35B-A3B-NVFP4Shares qwen3_5_moe · 8-bit · conversational | 4,104,345 | 678 | 18.7B | apache-2.0 | |
| Qwen3.6-35B-A3BShares qwen3_5_moe · image-text-to-text · conversational | 4,071,952 | 3,119 | 36B | apache-2.0 |
alchaincyf · Skill
Turns a one-line request in your AI agent into a finished design: a launch animation, a clickable app prototype, an editable slide deck, an infographic or a se…
op7418 · Skill
Skill that makes single-file HTML slide decks, slide illustrations and platform covers in two visual styles, with a presenter mode for rehearsal. For speakers…
alirezarezvani · Skill
Adds 411 ready-made skills for Claude Code and other coding agents, grouped by role from engineering and product to marketing, finance and compliance. Installs…
block · Kit
Self-hostable workspace where people and AI agents share the same rooms, built on a Nostr relay that logs every action as a signed event. For teams that want t…
backnotprop · Kit
Opens your AI coding agent's plans, specs and code changes in a browser so you can annotate or comment on them and send the feedback straight back to the agent.
alibaba · Kit
Reviews Git changes with an AI agent that reads files and searches the codebase, then writes comments tied to exact lines. It suits developers who want automat…