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 / TRAINING_DATA.md

philipjohnbasile

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Training Data — provenance · licenses · contamination (ledger)

Honest record of what the model was healed on, where it came from, and proof the reported numbers aren't memorized. (The model is released; the heal data itself is not published.)

Heal / SFT sources

External open datasets (via scripts/27_build_heal_data.py, scripts/06_heal_lora.py) + self-generated/verified data + hand-authored gold seeds.

Dataset	Use	License (verify before redistributing the data)
`open-r1/Mixture-of-Thoughts`	reasoning + code	Apache-2.0
`open-r1/OpenR1-Math-220k`	math reasoning	Apache-2.0
`open-thoughts/OpenThoughts-114k`	reasoning	Apache-2.0
`HuggingFaceH4/ultrachat_200k`	general chat	MIT
`theblackcat102/evol-codealpaca-v1`	instruction→code	Apache-2.0 ⚠ Evol/GPT-distilled — check model-output terms
`Salesforce/xlam-function-calling-60k`	tool-calling	CC-BY-4.0
`glaiveai/glaive-function-calling-v2`	tool-calling	Apache-2.0 (verify)
`glaiveai/reasoning-v1-20m`	reasoning	(verify)
`SWE-bench/SWE-smith-trajectories`	agentic / tool	MIT (verify)
`internlm/Lean-Workbook`	Lean proofs	Apache-2.0 (verify)

Self-generated / hand-authored (ours → MIT): the Lean expert-iteration flywheel output, the design-soul + 7-facet gold seeds (heal/facets/seeds/, heal/design/seeds/), CallSieve retrieval data, verifier-mesh RFT.

Eval benchmarks — TESTING only, never trained on

openai/openai_humaneval (MIT) · openai/gsm8k (MIT) · mbpp (CC-BY) · miniF2F (MIT). Loaded by 58_bench / 59_stem_diag / 73_minif2f for evaluation, not heal.

Contamination — verified CLEAN (CPU, 2026-06-18)

The honest risk: the big reasoning/code datasets above can contain benchmark problems → memorized, not reasoned. Checked with scripts/81_contamination_check.py (miniF2F vs Lean training) and scripts/82_heal_benchmark_contam.py (benchmarks vs the 236 M-char heal corpus):

Benchmark	Present in training?	Verdict
miniF2F-test (226)	0 exact · 1 near-dup (0.4 %)	✓ honest
HumanEval (164)	0.0 %	✓ the 19/20 is honest
GSM8K-test (sampled 300)	0.0 %	✓ honest

Method: name-agnostic normalized exact-match + token-Jaccard near-dup (miniF2F) and normalized prompt-substring (HumanEval/GSM8K) — verbatim/near-verbatim inclusion would be caught. Reported numbers are reasoned, not memorized.

License stance

Self-generated + hand-authored → MIT (matches the model + base zai-org/GLM-5.2, MIT). External-dataset portions retain their upstream licenses (table). The released model is a derivative; verify each dataset's license before redistributing the heal data itself.