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

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

16ba26f

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1 Parent(s): eac8caf

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@@ -14,8 +14,8 @@ tags: [mlx, moe, code, agentic, glm, pruned, quantized, verified-decoding, apple
 **One line:** we took `zai-org/GLM-5.2` (743B-parameter Mixture-of-Experts, ~381 GB at 4-bit) and
 demolished it to **99 GB** so it runs **fully on-device on a MacBook Pro M5 Max (128 GB)** — then
 healed it and wrapped it in a **51-tool local agent** that does things a cloud model structurally
-cannot: the **compiler steers every line it writes**, it **can't fake a passing test or leak a
-secret**, and it can be **fine-tuned on *your* private repo** so it writes in your style.
 A **niche specialist**, not a general model — tuned to beat a frontier model *in one lane* (agentic
 coding + design for **TS/JS/Python/Rust/Go/HTML/CSS** + Postgres) by out-*verifying* it, not out-knowing it.
@@ -37,8 +37,8 @@ whole stack of those roles (strongest in the coding/agentic lane, deliberately s
 …**and the hats that fall straight out of "verify-everything":**
 - **Security / DevSecOps** — secret-scanning (16 providers: AWS/GitHub/OpenAI/**Anthropic/HuggingFace**/Slack/Stripe/Google/DB-URLs/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 +

 **One line:** we took `zai-org/GLM-5.2` (743B-parameter Mixture-of-Experts, ~381 GB at 4-bit) and
 demolished it to **99 GB** so it runs **fully on-device on a MacBook Pro M5 Max (128 GB)** — then
 healed it and wrapped it in a **51-tool local agent** that does things a cloud model structurally
+cannot: the **compiler steers every line it writes**, it **re-verifies tests on `done`** and
+**blocks known-format secret writes**, and it can be **fine-tuned on *your* private repo** so it writes in your style.
 A **niche specialist**, not a general model — tuned to beat a frontier model *in one lane* (agentic
 coding + design for **TS/JS/Python/Rust/Go/HTML/CSS** + Postgres) by out-*verifying* it, not out-knowing it.
 …**and the hats that fall straight out of "verify-everything":**
 - **Security / DevSecOps** — secret-scanning (16 providers: AWS/GitHub/OpenAI/**Anthropic/HuggingFace**/Slack/Stripe/Google/DB-URLs/JWT/PEM…),
+  prompt-injection guard, test-tamper flag + **fabrication-proof `done`** (re-runs the *original* tests), slopsquat/typosquat guard, risk-gated
+  tools. It **blocks known-format secret writes + reward-hacking on the common paths** (pattern-based — a strong guard, not a vault/sandbox replacement).
 - **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 +