Instructions to use skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit 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("skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit")
config = load_config("skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit")

# 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

How to use skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit 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 "skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit"

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 skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Mistral-Small-3.2-24B-Instruct-2506 — MLX 8-bit (vision-preserving)

An 8-bit MLX quantization of mistralai/Mistral-Small-3.2-24B-Instruct-2506 that keeps the vision tower — a full text + image VLM for Apple Silicon via mlx-vlm.

Why this exists

The other MLX community quants of this model are text-only — the vision tower and chat template were dropped. This one preserves vision: 218 vision_tower tensors, preprocessor_config.json, and chat_template.jinja are all included, so image understanding works, not just text.

Details

Base: mistralai/Mistral-Small-3.2-24B-Instruct-2506 (Apache-2.0)
Converted from unsloth/Mistral-Small-3.2-24B-Instruct-2506: the mistralai repo ships Mistral-native format with no HF preprocessor_config.json, so a straight convert drops vision; unsloth's re-upload includes the HF processor + tokenizer configs this quant needs.
Quant: 8-bit, ~8.85 bits/weight (mlx_vlm.convert -q --q-bits 8)
Arch: mistral3 (Pixtral-style vision encoder) — the image processor needs torch+torchvision.

Use

pip install mlx-vlm torch torchvision
python -m mlx_vlm.server --model skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit --port 8080
# then POST OpenAI-style chat completions with text and/or image_url content

Verified: correctly edits text and describes images.

Downloads last month: 293

Safetensors

Model size

8B params

Tensor type

BF16

U32

MLX

Hardware compatibility

8-bit

Model tree for skibare87/Mistral-Small-3.2-24B-Instruct-2506-mlx-8bit

Base model

mistralai/Mistral-Small-3.1-24B-Base-2503

Finetuned

mistralai/Mistral-Small-3.2-24B-Instruct-2506

Quantized

(67)

this model