Instructions to use philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX"

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": "philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX 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 "philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX"

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 philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX

Run Hermes

hermes

OpenClaw new

How to use philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX"

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 "philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX" \
  --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"

MLX LM

How to use philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "philipjohnbasile/GLM-5.2-Demolition-q4a4-soul-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

philipjohnbasile commited on 15 days ago

Commit

7c04618

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1 Parent(s): 5a086fc

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@@ -31,9 +31,22 @@ whole stack of those roles (strongest in the coding/agentic lane, deliberately s
   math (GSM8K-style), and **code-rendered figures** (matplotlib / manim / TikZ).
 - **Machine Learning** — it *is* applied ML end-to-end: **REAP expert-pruning** (256→77), **mixed-precision
   quantization**, **LoRA healing**, **distillation**, **MTP self-speculation**, GRPO/RLVR experiments — the
-  build itself is a working reference — plus a local **Lean-4 formal-math** lane (correct-by-construction).
-One model, fully local, **verify-everything**: the four hats I wear, on a MacBook.
 ## How it was made
 1. **Pruned** the MoE experts 256 → 77 by **router-weighted saliency (REAP** = `router_weight ×

   math (GSM8K-style), and **code-rendered figures** (matplotlib / manim / TikZ).
 - **Machine Learning** — it *is* applied ML end-to-end: **REAP expert-pruning** (256→77), **mixed-precision
   quantization**, **LoRA healing**, **distillation**, **MTP self-speculation**, GRPO/RLVR experiments — the
+  build itself is a working reference.
+…**and the hats that fall straight out of "verify-everything":**
+- **Security / DevSecOps** — secret-scanning (12 providers: AWS/GitHub/OpenAI/Slack/Stripe/JWT/PEM…),
+  prompt-injection guard, test-tamper + **fabrication-proof `done`**, slopsquat/typosquat guard, risk-gated
+  tools. It structurally **can't leak a key or fake a green test**.
+- **Formal-Methods / Verification Engineer** — a local **Lean-4** prover (premise selection, expert-iteration,
+  self-correction from the *real* Lean error) → **correct-by-construction** math, not vibes.
+- **MLOps / Inference** — the serving spine: prompt-cache, continuous batching, watchdog + circuit-breaker +
+  memory-ceiling — **frontier-grade stability** for hours-long local runs on one box.
+- **Multimodal / CV** — reads images + video (VLM), palette-steered **image-gen**, code-rendered
+  video/figures (**manim/TikZ**) — all MLX.
+- **Design Engineer** — a render-and-***see*** critic enforcing **WCAG** contrast, modular type scale,
+  8 px grid, **OKLCH** harmony (not just "looks fine").
+One model, fully local, **verify-everything** — every hat above, on a MacBook.
 ## How it was made
 1. **Pruned** the MoE experts 256 → 77 by **router-weighted saliency (REAP** = `router_weight ×