Text Generation
MLX
Safetensors
English
glm_moe_dsa
apple-silicon
Mixture of Experts
pruned
quantized
soul-targeted
agentic
local-agent
glm
conversational
Eval Results (legacy)
4-bit precision
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
- MLX
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
- Pi
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"} ] }'
File size: 3,106 Bytes
66f10a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | # βοΈ Report 2 β soul2 SHIPPED (2026-06-19)
**TL;DR:** The 250-example multi-facet **soul2** healed, scored **GREEN**, and **shipped**. Code held exactly
(HumanEval-164 = **116/164 = 70.7%**, identical to soul v1) while the corpus added science Β· perfumery Β·
deep-security Β· pentest Β· the full design-movement range. The per-facet flywheel confirmed a **known limitation
(3-bit long-generation degeneration), not a regression.**
## Verdict: GREEN β β shipped
- **`adapters-soul2`** uploaded to HF (commit `859ed1a`).
- **HumanEval-164: 116/164 = 70.7%** β *exactly* soul v1. The 250-corpus (vs v1's 43) **preserved code capability
while adding ~6Γ the breadth.** No overfitting collapse despite train loss 0.093 / val 1.22.
## The honest flywheel finding (long-gen degeneration, NOT a soul2 regression)
Per-facet self-generation scorecard (partial β stopped once the signal was unambiguous):
- **design: 0 elite / 24 reject** β fully degenerate on long HTML.
- **code: 2 elite / 13 reject** β and the tell is decisive: the **2 elite gens were SHORT (~1258 ch); the 13
rejects were LONG (5β7K ch)** and collapsed into repetition + corrupted tokens (`UTF.FF9B`-style garble).
This is the documented **3-bit Computation Collapse on long generation** (`research/swappable_adapters_sota.md`,
rounds 4β5): the model degenerates *when it rambles* β **not** a soul2 regression (soul v1 degenerated identically).
The **gold/soul is elite** β the short elite gens prove it, and the masters-corpus is sound. The collapse is a
*serving/decoding* artifact of 3-bit on long output, which is exactly **why the soul is masters-GOLD, not self-gen.**
**The fixes (research-backed, queued):**
1. **Saliency-dynamic quant (#59)** β protect the salient + early-layer experts at 4-bit+, the rest at 3-bit. The
research is blunt that Computation Collapse is fixable *only* by mixed-precision on the critical experts, not a decoding trick.
2. **Shorter CoT** β `scripts/08_think_proxy.py`'s reasoning budget (already built): short = elite, long = collapse.
## Corpus
- **soul2:** 250 masters-gold (8 facets) β SHIPPED.
- **+266 specialty gold** ready for the next round (the factory's modules): fullstack-AI-DS (8 areas), gamedev
(Unreal/Unity/Godot/Flutter/patterns/gfx-net), legacy (COBOL/Java/PHP/Perl/VB + modern Java 21/PHP 8.4/.NET 8),
cyber-core + pentest (purple-team), science, perfumery, factory-router.
## Next
Heal the specialty adapters (GPU now freed): **core-soul-v3** (non-code + science + perfumery + deep-security +
pentest + router) first, then the swappable code modules (fullstack / gamedev / legacy), using **MoE-Sieve placement**
(top-25% routed experts β ~70% smaller adapters) + **iw-SFT** β both from the research.
## Provenance
9-round June-2026 SOTA scan β `research/swappable_adapters_sota.md` + `IMPLEMENTATION_PLAN.md`. The verdict, the
collapse diagnosis, and the validations of our core calls (prune > merge, verifier-mesh over self-reward,
on-policy-KD now industry-standard, the DSA top-2048 = our heal limit) are all recorded there.
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