Instructions to use MedMLX/MLX-Reason-CT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MedMLX/MLX-Reason-CT 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("MedMLX/MLX-Reason-CT")
config = load_config("MedMLX/MLX-Reason-CT")

# 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 MedMLX/MLX-Reason-CT with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "MedMLX/MLX-Reason-CT"

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": "MedMLX/MLX-Reason-CT"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use MedMLX/MLX-Reason-CT 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 "MedMLX/MLX-Reason-CT"

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 MedMLX/MLX-Reason-CT

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use MedMLX/MLX-Reason-CT with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "MedMLX/MLX-Reason-CT"

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 "MedMLX/MLX-Reason-CT" \
  --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"

josand commited on 10 days ago

Commit

2ce9770

verified ·

1 Parent(s): 351d51a

Label the retained historical validation records

Browse files

Files changed (1) hide show

README.md +4 -0

README.md CHANGED Viewed

@@ -129,6 +129,10 @@ EOS reached, text and JSON outputs consistent). This verifies execution and the
 documented component contracts; it does not establish clinical accuracy or
 full-model agreement with the original BF16 implementation.
 Version 0.2.3 includes faster source-BF16 projection and attention kernels.
 On one M1 Max synthetic case, cached full-model time fell from 17:54 in the
 preceding source build to 14:49, with bitwise-unchanged recorded outputs.

 documented component contracts; it does not establish clinical accuracy or
 full-model agreement with the original BF16 implementation.
+The retained `native-run.json` (2026-10-08) and `reference-comparison.json`
+(2026-10-12) describe historical executions at their recorded code revisions.
+The current release checks are the `v0.3.2` checks described above.
 Version 0.2.3 includes faster source-BF16 projection and attention kernels.
 On one M1 Max synthetic case, cached full-model time fell from 17:54 in the
 preceding source build to 14:49, with bitwise-unchanged recorded outputs.