Instructions to use LibraxisAI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LibraxisAI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4 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/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4")
config = load_config("LibraxisAI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4")

# 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 Settings
LM Studio

Unsloth Studio

How to use LibraxisAI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LibraxisAI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LibraxisAI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LibraxisAI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="LibraxisAI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4",
    max_seq_length=2048,
)

How to use LibraxisAI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4 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/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4"

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/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use LibraxisAI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4 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/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4"

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/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4

Run Hermes

hermes

div0-space commited on May 12

Commit

ef53aee

verified ·

1 Parent(s): 345c374

Update README.md

Browse files

Clarify the inference setup and perf stats

Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -10,7 +10,6 @@ tags:
 - image-text-to-text
 - vision
 - multimodal
-- vmlx
 - vlm
 - reasoning
 - distillation
@@ -28,6 +27,7 @@ tags:
 - huihui
 - quantized
 - mxfp4
 inference: false
 widget:
 - text: Summarize the operational risks in this deployment plan.
@@ -36,12 +36,12 @@ widget:
 # Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4
-`Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4` is an MLX / VMLX vision-language checkpoint derived from `huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated`, packaged for local multimodal experimentation on Apple Silicon.
 ## 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 multimodal mlx-lm inference on Apple Silicon.
 | Aspect | Status |
 |---|---|
@@ -63,7 +63,7 @@ This does not claim compatibility with every possible serving stack. It document
 - Safety-critical decisions without domain expert review
 - Claims of benchmark superiority not backed by published evaluation data
-- Non-MLX / non-VMLX runtime guarantees
 - High-stakes visual interpretation without human validation
 ## Training and conversion metadata
@@ -96,4 +96,4 @@ Use the library instructions above, or run this checkpoint through the tested lo
 ---
-𝚅𝚒𝚋𝚎𝚌𝚛𝚊𝚏𝚝𝚎𝚍. with AI Agents by VetCoders (c)2024-2026 LibraxisAI

 - image-text-to-text
 - vision
 - multimodal
 - vlm
 - reasoning
 - distillation
 - huihui
 - quantized
 - mxfp4
+- mlx-vlm
 inference: false
 widget:
 - text: Summarize the operational risks in this deployment plan.
 # Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4
+`Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-vmlx-mxfp4` is an `mlx-vlm` checkpoint derived from `huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated`, packaged for local multimodal experimentation on Apple Silicon.
 ## 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 multimodal mlx-lm / mlx-vlm inference on Apple Silicon.
 | Aspect | Status |
 |---|---|
 - Safety-critical decisions without domain expert review
 - Claims of benchmark superiority not backed by published evaluation data
+- Non-MLX runtime guarantees
 - High-stakes visual interpretation without human validation
 ## Training and conversion metadata
 ---
+𝚅𝚒𝚋𝚎𝚌𝚛𝚊𝚏𝚝𝚎𝚍. with AI Agents by VetCoders (c)2024-2026 LibraxisAI