README.md

---
license: apache-2.0
language:
- en
- pl
- multilingual
base_model:
- huihui-ai/Huihui4-48B-A4B-abliterated
library_name: mlx
pipeline_tag: image-text-to-text
tags:
- mlx
- apple-silicon
- gemma
- gemma4
- gemma-4
- abliterated
- uncensored
- moe
- multimodal
- vision
- image-text-to-text
- vmlx
- nvfp4
- 4bit
- quantized
- huihui
inference: false
---

# Huihui4-48B-A4B-vmlx-nvfp4

`Huihui4-48B-A4B-vmlx-nvfp4` is an MLX vision-language checkpoint derived from `huihui-ai/Huihui4-48B-A4B-abliterated`, packaged for local multimodal prompting on Apple Silicon.

## Intended use

- Local image-and-text reasoning on Apple Silicon
- Document, screenshot, chart, and visual question answering experiments
- Operator-controlled multimodal prototyping where hosted inference is not desired

## Out of scope

- Safety-critical decisions without domain expert review
- Claims of benchmark superiority not backed by published evaluation data
- Non-MLX runtime guarantees; this card documents the shipped HF checkpoint, not every possible serving stack
- High-stakes visual interpretation without human review

## Training and conversion metadata

| Parameter | Value |
|---|---|
| Repository | `LibraxisAI/Huihui4-48B-A4B-vmlx-nvfp4` |
| Base model | `huihui-ai/Huihui4-48B-A4B-abliterated` |
| Task | `image-text-to-text` |
| Library | `mlx` |
| Format | MLX / Apple Silicon checkpoint |
| Quantization | NVFP4 |
| Architecture | Gemma4ForConditionalGeneration |
| Model files | 6 |
| Config model_type | `gemma4` |

This card only reports metadata present in the Hugging Face repository, existing card frontmatter, or public config files. Missing benchmark, dataset, or training-run details are left explicit rather than reconstructed.

## Tested inference path

> **Inference for this checkpoint has been tested with [`LibraxisAI/mlx-batch-server`](https://github.com/LibraxisAI/mlx-batch-server).**\
> This is the recommended tested path for operator-controlled local inference on Apple Silicon.

| Aspect | Status |
|---|---|
| Tested runtime | `LibraxisAI/mlx-batch-server` |
| Target hardware | Apple Silicon |
| Inference mode | Local / self-hosted |
| Hugging Face Hosted Inference | Disabled for this repository (`inference: false`) |

This does not claim compatibility with every possible serving stack. It documents the path that has been exercised for this published checkpoint.

## Usage

### CLI

```bash
pip install mlx-vlm

python -m mlx_vlm.generate \
  --model LibraxisAI/Huihui4-48B-A4B-vmlx-nvfp4 \
  --image image.jpg \
  --prompt "Summarize the key signals in this document and list the next action items." \
  --max-tokens 256
```

### Python

```python
from mlx_vlm import generate, load

model, processor = load("LibraxisAI/Huihui4-48B-A4B-vmlx-nvfp4")
response = generate(
    model,
    processor,
    prompt="Summarize the key signals in this document and list the next action items.",
    image="image.jpg",
    max_tokens=256,
)
print(response)
```

## Example output

No public sample output is currently declared for this checkpoint.

## Quantization notes

| Aspect | Original/base checkpoint | This checkpoint |
|---|---|---|
| Lineage | `huihui-ai/Huihui4-48B-A4B-abliterated` | `LibraxisAI/Huihui4-48B-A4B-vmlx-nvfp4` |
| Runtime target | Upstream runtime format | MLX on Apple Silicon |
| Quantization | Base precision or upstream-declared format | NVFP4 |
| Published quality delta | Not declared in public metadata | Not declared in public metadata |

## Limitations

- No public benchmarks for this checkpoint are declared in the model metadata.
- No public benchmark claims are made by this card unless listed in the frontmatter.
- Validate outputs on your own domain data before relying on this checkpoint.
- Memory use and speed depend heavily on the exact Apple Silicon generation, unified-memory size, and prompt length.

## License

`apache-2.0`. Check the upstream/base model license as well when a base model is declared.

## Citation

```bibtex
@misc{libraxisai-huihui4-48b-a4b-vmlx-nvfp4,
  title = {Huihui4-48B-A4B-vmlx-nvfp4},
  author = {LibraxisAI},
  year = {2026},
  howpublished = {\url{https://huggingface.co/LibraxisAI/Huihui4-48B-A4B-vmlx-nvfp4}},
  note = {MLX checkpoint published by LibraxisAI}
}
```
---

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