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"}
     ]
   }'

GLM-5.2-Demolition-q4a4-soul-MLX / AGENTIC.md

philipjohnbasile

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Run this as your local agentic coder

The pitch: not the highest SWE-bench — the most reliable. The local agentic coder that can't break: zero malformed tool-calls (grammar-enforced, structurally impossible), compiler-steered every line, fabrication-proof done (re-runs the real tests — can't fake a pass), and elite across the whole stack (design · math · security · science), not just code. Reliability is the moat raw capability can't buy.

Serve it (OpenAI-compatible, on your Mac)

# raise the GPU ceiling once (or long agentic runs OOM):
sudo sysctl iogpu.wired_limit_mb=122000
# serve base + the core soul (OpenAI-compatible on :8080):
GLM_STREAM_EVAL=0 python -m mlx_lm.server --model models/GLM-5.2-q3a4-v4 \
    --adapter-path adapters-soul2 --port 8080
# optional: concise-thinking proxy (caps reasoning tokens -> faster, cleaner tool turns):
python scripts/08_think_proxy.py --port 8081 --upstream http://localhost:8080

Point your agent at it

Anything that speaks OpenAI-compatible drops in against http://localhost:8080/v1 (or :8081 for the think-proxy), any model name:

Agent	Setup
Cline (VS Code)	Provider: OpenAI Compatible · Base URL `http://localhost:8080/v1` · any model id
Aider	`aider --openai-api-base http://localhost:8080/v1 --openai-api-key x --model glm-demolition`
OpenCode	add an OpenAI-compatible provider pointing at `:8080/v1`
Cursor	Settings → Models → override OpenAI Base URL to `:8080/v1`
Claude Code	needs an OpenAI→Anthropic shim (or `rapid-mlx`'s `ANTHROPIC_BASE_URL` trick) — Claude Code speaks the Anthropic API, not OpenAI

Recommended settings: temperature 0.6, top_p 0.95 for coding; enable_thinking on for hard problems, off (or the think-proxy budget) for fast tool turns.

Why it doesn't break (the reliability stack)

Grammar-constrained tool-JSON — invalid tokens get zero probability at each step, so a malformed call is structurally impossible (vs the field's best: "fewer malformed"). Speaks the 2026 strict-schema + MCP conventions.
Verified / compiler-steered decoding — a line that adds a type error is backtracked as it's written.
Fabrication-proof done — the agent re-runs the original tests before claiming success; it can't hallucinate a pass.
Integrity layer — test-tamper guard, 16-provider secret-scan, scope enforcement, slopsquat guard.
51-tool ReAct agent — trajectory compaction + stall detection for long-horizon runs; the verifier mesh checks every output against its real tool.

Honest limits (and what's queued to fix them)

~11–14 tok/s decode — the memory trade for a local 743B-class model. Queued: NVFP4 + M5 Neural Accelerators (~2× decode on M5), throughput batching for concurrent requests (2.6× at B=8 today).
Long single generations can degenerate at 3-bit (Computation Collapse). Queued: saliency-dynamic quant (#59 — early/late experts at 4-bit) + a serve-layer auto-recovery that re-structures broken tool-output.
Needs a 128 GB Mac — premium tier for now; a 64 GB sibling is deferred (depth before breadth).

The trade we made on purpose: less raw benchmark, in exchange for reliability + breadth + fully local + verify-everything — the things that actually decide whether an agent finishes the job.