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8-bit MLX quantization of microsoft/GELab-Zero-4B-preview-Sico-Evolution — Microsoft's Qwen3-VL-based GUI agent (Edge / Copilot UI trajectories, from the Sico evolution pipeline) — running natively on Apple Silicon...
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8-bit MLX quantization of microsoft/GELab-Zero-4B-preview-Sico-Evolution — Microsoft's Qwen3-VL-based GUI agent (Edge / Copilot UI trajectories, from the Sico evolution pipeline) — running natively on Apple Silicon via mlx-vlm.
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1 fonteVerificado 18 de set.
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos| Source | microsoft/GELab-Zero-4B-preview-Sico-Evolution @ 517c05402871b7c3020eab0925c7e0f9c2b02248 |
| Architecture | Qwen3VLForConditionalGeneration (36-layer text tower, 24-layer vision tower) |
| Format | MLX (Apple Silicon native), mlx-vlm loadable |
| Quantization | 8-bit, group-size 64 (9.20 bits/weight effective) |
| Disk size | 4.8 GB |
Converted with mlx-vlm 0.3.9 and image-inference smoke-tested locally before publishing — the BF16 source and this quantized pack both correctly describe the model's own benchmark chart:
"The image displays a bar chart comparing the Task Success Rate (TSR) of various models in Edge/Copilot test cases."
| Variant | Size | Status |
|---|---|---|
| GELab-Zero-4B-preview-Sico-Evolution-MLX-8bit | 4.8 GB | public |
| GELab-Zero-4B-preview-Sico-Evolution-MLX-6bit | 3.8 GB | public |
| GELab-Zero-4B-preview-Sico-Evolution-MLX-4bit | 2.9 GB | public |
Requires mlx-vlm >= 0.3.9:
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
model, processor = load("majentik/GELab-Zero-4B-preview-Sico-Evolution-MLX-8bit")
prompt = apply_chat_template(processor, model.config, "Describe this screenshot.", num_images=1)
print(generate(model, processor, prompt, ["screenshot.png"], max_tokens=256))
python -m mlx_vlm generate \
--model majentik/GELab-Zero-4B-preview-Sico-Evolution-MLX-8bit \
--image screenshot.png --prompt "What action should I take next?"
GUI-agent tasks — screenshot understanding, UI grounding, and next-action prediction for desktop/web automation — locally on Apple Silicon. This is the Edge/Copilot-evolved variant; see the base model card for benchmarks (82.9% TSR) and the Sico pipeline.
python -m mlx_vlm convert --hf-path microsoft/GELab-Zero-4B-preview-Sico-Evolution \
--mlx-path GELab-8bit -q --q-bits 8 --q-group-size 64
Apache-2.0, inherited from the upstream model. This repository redistributes quantized weights with attribution; training and evaluation details are on the base model card.
--- license: apache-2.0 library_name: mlx pipeline_tag: image-text-to-text tags: - mlx - qwen3-vl - gui-agent - vision-language - quantized - 8-bit - apple-silicon base_model: microsoft/GELab-Zero-4B-preview-Sico-Evolution language: - en - zh --- # GELab-Zero-4B-preview-Sico-Evolution-MLX-8bit 8-bit MLX quantization of [microsoft/GELab-Zero-4B-preview-Sico-Evolution](https://huggingface.co/microsoft/GELab-Zero-4B-preview-Sico-Evolution) — Microsoft's Qwen3-VL-based **GUI agent** (Edge / Copilot UI trajectories, from the [Sico](https://github.com/microsoft/Sico) evolution pipeline) — running natively on Apple Silicon via `mlx-vlm`. ## At a glance | | | |---|---| | **Source** | `microsoft/GELab-Zero-4B-preview-Sico-Evolution` @ `517c05402871b7c3020eab0925c7e0f9c2b02248` | | **Architecture** | `Qwen3VLForConditionalGeneration` (36-layer text tower, 24-layer vision tower) | | **Format** | MLX (Apple Silicon native), `mlx-vlm` loadable | | **Quantization** | 8-bit, group-size 64 (9.20 bits/weight effective) | | **Disk size** | **4.8 GB** | ## Verification (2026-07-12, M-series Mac) Converted with `mlx-vlm` 0.3.9 and **image-inference smoke-tested locally** before publishing — the BF16 source and this quantized pack both correctly describe the model's own benchmark chart: > *"The image displays a bar chart comparing the Task Success Rate (TSR) of various models in Edge/Copilot test cases."* ## Sibling variants | Variant | Size | Status | |---|---|---| | **GELab-Zero-4B-preview-Sico-Evolution-MLX-8bit** | 4.8 GB | public | | [GELab-Zero-4B-preview-Sico-Evolution-MLX-6bit](https://huggingface.co/majentik/GELab-Zero-4B-preview-Sico-Evolution-MLX-6bit) | 3.8 GB | public | | [GELab-Zero-4B-preview-Sico-Evolution-MLX-4bit](https://huggingface.co/majentik/GELab-Zero-4B-preview-Sico-Evolution-MLX-4bit) | 2.9 GB | public | <!-- sibling-table --> ## Usage Requires `mlx-vlm >= 0.3.9`: ```python from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template model, processor = load("majentik/GELab-Zero-4B-preview-Sico-Evolution-MLX-8bit") prompt = apply_chat_template(processor, model.config, "Describe this screenshot.", num_images=1) print(generate(model, processor, prompt, ["screenshot.png"], max_tokens=256)) ``` ```bash python -m mlx_vlm generate \ --model majentik/GELab-Zero-4B-preview-Sico-Evolution-MLX-8bit \ --image sc...
Source context: 4 downloads · 0 likes · Pipeline image-text-to-text · Library mlx · Repo majentik/GELab-Zero-4B-preview-Sico-Evolution-MLX-8bit