abenzerps / Holo4-35B-A3B-MLX

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Model's Last Updated: September 29 2026
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Introduction of Holo4-35B-A3B-MLX

Model Details of Holo4-35B-A3B-MLX

Holo4-35B-A3B MLX

MLX quantizations of Hcompany/Holo4-35B-A3B , a 35B mixture-of-experts (MoE, 3B active) vision-language model (VLM) for Computer Use, tool-driven work, and agentic workflows.

Each quantization includes the full multimodal vision tower (333 visual weights + vision_config ), enabling native image and screenshot understanding in MLX-VLM, LM Studio, and Apple Silicon workflows.

Upstream benchmarks

Holo4-35B-A3B benchmark results

Results reported by H Company from evaluations of the original Holo4-35B-A3B model across computer tasks, long workflows, and tool servers.

MLX Files
Quantization File Size
4-bit Holo4-35B-A3B-MLX-4bit 19.00 GB
6-bit Holo4-35B-A3B-MLX-6bit 27.07 GB
8-bit Holo4-35B-A3B-MLX-8bit 35.13 GB
Multimodal Architecture
Component Architecture Precision
Language Backbone Qwen3.5 MoE (35B total, 3B active) Quantized (4/6/8-bit affine, group_size=64)
Vision Tower Vision Transformer (333 weights) Full precision (BF16)
Projector Multimodal cross-attention / MLP Full precision (BF16)

Vision tower weights and multimodal projectors are preserved in full precision (BF16) to ensure optimal visual comprehension, OCR, and GUI element grounding without degradation.

Usage with MLX-VLM
Installation
pip install -U mlx-vlm
Python API
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load 4-bit (or subfolder="Holo4-35B-A3B-MLX-6bit", subfolder="Holo4-35B-A3B-MLX-8bit")
model_path = "abenzerps/Holo4-35B-A3B-MLX"
subfolder = "Holo4-35B-A3B-MLX-4bit"

model, processor = load(model_path, subfolder=subfolder)
config = load_config(model_path, subfolder=subfolder)

prompt = "Describe the user interface elements shown in this screenshot."
image = ["screenshot.png"]

formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=len(image)
)

output = generate(model, processor, formatted_prompt, image, verbose=True)
print(output)
Command Line Interface
# 4-bit
python -m mlx_vlm.generate \
    --model abenzerps/Holo4-35B-A3B-MLX --subfolder Holo4-35B-A3B-MLX-4bit \
    --image screenshot.png \
    --prompt "What action should be taken next to achieve the user goal?"

# 6-bit
python -m mlx_vlm.generate \
    --model abenzerps/Holo4-35B-A3B-MLX --subfolder Holo4-35B-A3B-MLX-6bit \
    --image screenshot.png \
    --prompt "What action should be taken next to achieve the user goal?"

# 8-bit
python -m mlx_vlm.generate \
    --model abenzerps/Holo4-35B-A3B-MLX --subfolder Holo4-35B-A3B-MLX-8bit \
    --image screenshot.png \
    --prompt "What action should be taken next to achieve the user goal?"
Source

Runs of abenzerps Holo4-35B-A3B-MLX on huggingface.co

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https://huggingface.co/abenzerps/Holo4-35B-A3B-MLX

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https://huggingface.co/abenzerps/Holo4-35B-A3B-MLX

Holo4-35B-A3B-MLX install

Holo4-35B-A3B-MLX is an open source model from GitHub that offers a free installation service, and any user can find Holo4-35B-A3B-MLX on GitHub to install. At the same time, huggingface.co provides the effect of Holo4-35B-A3B-MLX install, users can directly use Holo4-35B-A3B-MLX installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

Holo4-35B-A3B-MLX install url in huggingface.co:

https://huggingface.co/abenzerps/Holo4-35B-A3B-MLX

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Updated:October 02 2026
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Total runs: 0
Run Growth: 0
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Updated:October 02 2026