Instructions to use LibraxisAI/Huihui4-48B-A4B-vmlx-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use LibraxisAI/Huihui4-48B-A4B-vmlx-fp16 with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("LibraxisAI/Huihui4-48B-A4B-vmlx-fp16") config = load_config("LibraxisAI/Huihui4-48B-A4B-vmlx-fp16") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Pi new
How to use LibraxisAI/Huihui4-48B-A4B-vmlx-fp16 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "LibraxisAI/Huihui4-48B-A4B-vmlx-fp16"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "LibraxisAI/Huihui4-48B-A4B-vmlx-fp16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use LibraxisAI/Huihui4-48B-A4B-vmlx-fp16 with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "LibraxisAI/Huihui4-48B-A4B-vmlx-fp16"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default LibraxisAI/Huihui4-48B-A4B-vmlx-fp16
Run Hermes
hermes
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
- fp16
- bf16
- non-quantized
- huihui
- quantized
- bf16/fp16
inference: false
Huihui4-48B-A4B-vmlx-fp16
Huihui4-48B-A4B-vmlx-fp16 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-fp16 |
| Base model | huihui-ai/Huihui4-48B-A4B-abliterated |
| Task | image-text-to-text |
| Library | mlx |
| Format | MLX / Apple Silicon checkpoint |
| Quantization | BF16/FP16 |
| Architecture | Gemma4ForConditionalGeneration |
| Model files | 19 |
| 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.**
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
pip install mlx-vlm
python -m mlx_vlm.generate \
--model LibraxisAI/Huihui4-48B-A4B-vmlx-fp16 \
--image image.jpg \
--prompt "Summarize the key signals in this document and list the next action items." \
--max-tokens 256
Python
from mlx_vlm import generate, load
model, processor = load("LibraxisAI/Huihui4-48B-A4B-vmlx-fp16")
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-fp16 |
| Runtime target | Upstream runtime format | MLX on Apple Silicon |
| Quantization | Base precision or upstream-declared format | BF16/FP16 |
| 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
@misc{libraxisai-huihui4-48b-a4b-vmlx-fp16,
title = {Huihui4-48B-A4B-vmlx-fp16},
author = {LibraxisAI},
year = {2026},
howpublished = {\url{https://huggingface.co/LibraxisAI/Huihui4-48B-A4B-vmlx-fp16}},
note = {MLX checkpoint published by LibraxisAI}
}
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