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

9b0de50

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1 Parent(s): 381e5d2

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@@ -122,6 +122,22 @@ heals the **native** prior so it designs elite with no prompt at all.
 | Decode speed | **11.3 tok/s** (no draft) — see the speed note in limitations |
 | Verified-decode checker | TS 0.3 ms · Python ~0 ms · Rust 34 ms |
 ## Honest limitations
 - **Specialist:** ~70% of experts pruned — strong in the target niche, weaker outside it. Not the full 743B.
 - **Speed ~11 tok/s decode** (reading pace; ~3 min for long thinking-ON answers). Partly MLX's still-naive

 | Decode speed | **11.3 tok/s** (no draft) — see the speed note in limitations |
 | Verified-decode checker | TS 0.3 ms · Python ~0 ms · Rust 34 ms |
+## Roadmap — the Demolition family (shrink, keep the soul)
+Same masters-trained soul (design · dataviz · code · security · math · prose · architecture · research), every
+Mac — the elite training lives in the facet-inclusive calibration + heal corpus, which are **size-agnostic**:
+```
+  99GB  : ████████ (baseline, this model)
+  64GB  : should hold ~baseline   (96 GB Macs)
+  48GB  : should hold high        (64 GB Macs)
+  28GB  : the squeeze — watch which facets dip   (36-48 GB Macs)
+  14GB  : ⚗️ where does the soul start to break?  (24 GB Macs)
+   7GB  : ⚗️ the floor             (16 GB laptops)
+```
+Each size: facet-calib → prune harder → quantize → heal (the soul corpus) → soul-retention scorecard (% elite
+per facet). See [`design/DESIGN.md`](design/DESIGN.md).
 ## Honest limitations
 - **Specialist:** ~70% of experts pruned — strong in the target niche, weaker outside it. Not the full 743B.
 - **Speed ~11 tok/s decode** (reading pace; ~3 min for long thinking-ON answers). Partly MLX's still-naive