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Add files using upload-large-folder tool

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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,119 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: microsoft/AesCode-32B
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+ base_model_relation: finetune
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+ library_name: mlx
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+ pipeline_tag: image-text-to-text
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+ tags:
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+ - mlx
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+ - multimodal
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+ - vision
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+ - code-generation
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+ - html
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+ - apple-silicon
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+ - 8-bit
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+ language:
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+ - en
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+ license: apache-2.0
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+ ---
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+
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+ # AesCode-32B MLX 8bit
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+
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+ An 8-bit MLX quantization of
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+ [microsoft/AesCode-32B](https://huggingface.co/microsoft/AesCode-32B) for Apple
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+ Silicon, **with the vision tower kept in bf16 so it still sees**.
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+
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+ AesCode-32B is a `Qwen3VLForConditionalGeneration` finetune of
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+ [Qwen/Qwen3-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct)
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+ that takes a screenshot and writes the HTML and CSS to recreate it (slides,
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+ posters, dashboards, product pages). Only the language tower is
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+ quantized to 8-bit; the 351 `vision_tower.*` tensors stay in bfloat16, so the
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+ image path keeps its quality.
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+
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+ **8-bit is about half the size for smaller machines; bf16 is full quality.**
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+
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+ ## Sizes in this family
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+
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+ - [AesCode-32B-MLX-bf16 (full quality)](https://huggingface.co/divinetribe/AesCode-32B-MLX-bf16)
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+ - [AesCode-32B-MLX-8bit (half size)](https://huggingface.co/divinetribe/AesCode-32B-MLX-8bit)
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+ - [AesCode-32B-MLX-4bit (quarter size)](https://huggingface.co/divinetribe/AesCode-32B-MLX-4bit)
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+
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+ ## What is in this build
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+
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+ | | |
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+ |---|---|
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+ | precision | 8-bit language tower, bf16 vision tower |
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+ | vision | all 351 `vision_tower.*` tensors kept, `vision_config` and processor configs intact |
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+ | size | about 34 GB on disk |
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+ | framework | runs with `mlx-vlm` on Apple Silicon |
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+
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+ ## Vision was tested before upload
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+
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+ Every size of this model is put through the same gate before it ships: a real
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+ screenshot of a live ineedhemp.com product page is fed in and the model is asked
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+ for an SEO, marketing and accessibility audit plus image alt-text. A smaller build
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+ is only published if it still reads the page about as well as the full-precision
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+ one. This 8-bit build passed. From its audit of the real page:
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+
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+ > SEO: The main H1 is strong and includes the product name "Divine Crossing v5 Rebuildable Concentrate Heater". Marketing: the "Version 5 Ceramic Heater" callout, the star rating, the trust badges (shipping, warranty) and the "Add to Cart" button all read clearly. Alt-text: Main product image: "Divine Crossing v5 rebuildable concentrate heater in metallic finish with textured grip and glass chamber."
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+
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+ It named the real on-page elements and wrote usable alt-text, so the quantization
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+ did not break its vision.
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+
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+ ## Use
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+
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+ ```bash
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+ pip install mlx-vlm
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+ python -m mlx_vlm.generate --model divinetribe/AesCode-32B-MLX-8bit \
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+ --image screenshot.png \
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+ --prompt "Recreate this screenshot as a single HTML file with inline CSS." \
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+ --max-tokens 1200
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+ ```
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+
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+ Runs on an M-series Mac. No cloud inference, no API keys, no telemetry.
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+
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+ ## Benchmarks
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+
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+ The most authoritative evaluation that actually covers this model is the one in
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+ AesCode's own paper, **"AesCode: Aesthetic Code Generation with Decoupled
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+ Cross-Modal Rewards"** ([arXiv:2610.18860](https://arxiv.org/abs/2610.18860)).
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+ There is no independent public leaderboard that lists AesCode specifically, so
80
+ these numbers come from the authors' benchmark of 300 infographic-style samples,
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+ scored for rule-following correctness and visual quality. Scores quoted from the
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+ [model card](https://huggingface.co/microsoft/AesCode-32B):
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+
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+ | Model (with reference image) | Overall | Visual |
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+ |---|---|---|
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+ | **AesCode-32B** | **85.89** | 77.44 |
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+ | Claude Opus 4.8 (no reference) | 76.78 | |
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+ | GPT-5.5 (no reference) | 76.31 | |
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+ | Qwen3-VL-32B base (with reference) | 61.10 | |
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+
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+ These are the original model's numbers. For general-vision numbers on the backbone
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+ itself, see the official
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+ [Qwen3-VL](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct) evaluations (that is
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+ the base model's benchmark, not AesCode's).
95
+
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+ ## Credits
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+
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+ This build stands entirely on other people's work. Credit where it is due:
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+
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+ - **The AesCode team at Microsoft**, who created the model and the benchmark:
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+ Xin Gao, Yifan Yang, Xin Li, Pan Wang, Yan Li, Muzhao Tian, Mingxi Cheng, Bei
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+ Liu, Kai Qiu, Qi Dai, Dong Chen, Yue Dong, Leandros Tassiulas, Rex Ying, Ji Li
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+ and Chong Luo. Paper: [arXiv:2610.18860](https://arxiv.org/abs/2610.18860),
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+ model: [microsoft/AesCode-32B](https://huggingface.co/microsoft/AesCode-32B).
105
+ - **The Qwen team at Alibaba**, for the
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+ [Qwen3-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct) base.
107
+ - **The MLX team at Apple** and **Prince Canuma** and the
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+ [mlx-vlm](https://github.com/Blaizzy/mlx-vlm) contributors, for the open-source
109
+ tools that let a model like this run natively and privately on a Mac.
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+
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+ Thank you to all of them.
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+
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+ ## Note
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+
115
+ Quantization trades a little precision for size. It does not add knowledge and it
116
+ does not make output correct. You are responsible for what you build with it.
117
+
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+ Built and published by [divinetribe](https://huggingface.co/divinetribe) ·
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+ [more models](https://nicedreamzwholesale.com/software/huggingface/)
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