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"}
     ]
   }'

GLM-5.2-Demolition-q4a4-soul-MLX / research /agentic_coder_landscape.md

philipjohnbasile

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	# THE Local Agentic Coder — landscape + our path (June 2026)

	Goal: make this model THE model for local agentic coders. This is the competitive scan + the honest
	strategy that falls out of it.

	## The field (local/open agentic coders, June 2026)
	\| Model \| Agentic headline \| License \|
	\|---\|---\|---\|
	\| Qwen3-Coder-Next \| 58.7% SWE-bench-Verified, 256K ctx, runs on 24 GB — purpose-built for agentic loops; the local bar \| open \|
	\| DeepSeek-V4 (Pro 1.6T/49B · Flash 284B/13B) \| 1M ctx, long-horizon planning, improved tool-call reliability (fewer malformed JSON) \| MIT \|
	\| GLM-5.1 (our base's sibling, 754B) \| 58.4 SWE-Pro, 8-hour autonomous execution — SOTA agentic \| MIT \|
	\| Kimi K2.6 Thinking \| 78.57 coding / 58.33 agentic — strongest open \| Modified-MIT \|
	\| Qwen 3.6 Plus \| 1M ctx, reliable tool-use over long sessions \| open \|
	\| frontier ref: Claude Opus 4.6 \| 50% task completion @ 14.5-hour horizon \| closed \|

	They compete on: context length · raw SWE-bench · tool-call reliability · long-horizon autonomy · speed.

	## Where we honestly can't win
	Raw size (we demolished GLM-5.2 → 99 GB / 3-bit), decode speed (11–14 tok/s), context (vs 1M), raw SWE-bench
	(70 % of experts pruned). Chasing those is a losing race against un-demolished 24 GB models.

	## Where we WIN — agentic reliability (the thing that actually breaks agents)
	The research names tool-call reliability as the #1 agentic differentiator (DeepSeek-V4's headline win was
	"fewer malformed JSON / partial calls"). We don't improve it — we guarantee it:
	- Constrained tool-JSON (#45): grammar-enforced tool-calls → zero malformed JSON, structurally impossible. (Field's best = "fewer"; ours = "none.")
	- Compiler-steered / verified decoding (#21, #24): every line type-checks as it's written (TS 0.3 ms · Py ~0 · Rust 34 ms).
	- Fabrication-proof `done` (#41): re-runs the original tests → can't hallucinate a pass.
	- Integrity layer: test-tamper guard, secret-scan (16 providers), scope enforcement, slopsquat guard.
	- 51-tool ReAct agent: trajectory compaction, stall detection, the verifier mesh (5 langs + SQL + Lean).

	Positioning: *"the local agentic coder that can't malform a tool-call, can't fake a test pass, and
	compiler-checks every line."* The others are bigger/faster; none ship the verify-everything stack. Reliability
	is a moat raw capability can't buy — and it's exactly what makes long agentic runs not collapse.

	## Adoptable from the field (CPU now → heal later)
	1. Tool-use cold-start SFT (AgentRL / ProRL pattern): query-formulation → tool-invocation → valid-JSON →
	result-read → recover-from-error. → generate agentic tool-use gold for the next soul heal.
	2. Long-horizon agentic data (128 turns / 131K ctx, Hierarchy-of-Groups PO): multi-step plan→edit→test→fix
	trajectories that don't lose the thread. Our agent is built for it — train + benchmark it.
	3. Tool-call self-correction (DeepSeek-V4's win): recover from a bad call. (Our constrained decoder prevents
	the bad call upstream — but the recovery skill still helps when external tools fail.)
	4. Atomic skills ("Scaling Coding Agents via Atomic Skills", 2604.05013): decompose tasks into reusable skills → our skill library.
	5. MCP / OpenAI-compat integration: the field plugs local models into Cline · OpenCode · Aider · Claude Code via
	OpenAI-compat + MCP. We already serve OpenAI-compat (`mlx_lm.server`) — document the one-line setup per agent so we're a drop-in.

	## Plan
	- NOW (CPU): (a) position the card around agentic reliability; (b) generate agentic tool-use + long-horizon +
	reliability gold (`heal/gold_agentic/`) for the next heal; (c) document the Cline/OpenCode/Aider/Claude-Code drop-in setup.
	- LATER (GPU, behind the factory + #59): heal the agentic gold into the soul; benchmark long-horizon
	(Terminal-Bench / FeatureBench / real-task, not the saturating SWE-bench); the #59 collapse fix keeps long agentic gens from degenerating.

	Sources: Qwen3-Coder-Next / DeepSeek-V4 / GLM-5.1 (MarkTechPost, kilo.ai, mindstudio 2026) · Agentic Tool Use 2604.00835 · AgentRL/ProRL 2603.18815 · Hierarchy-of-Groups PO 2602.22817 · Atomic Skills 2604.05013 · CWM 2510.02387 · OpenCode/Cline (morphllm 2026).

	# THE Local Agentic Coder — landscape + our path (June 2026)

	Goal: make this model THE model for local agentic coders. This is the competitive scan + the honest
	strategy that falls out of it.

	## The field (local/open agentic coders, June 2026)
	\| Model \| Agentic headline \| License \|
	\|---\|---\|---\|
	\| Qwen3-Coder-Next \| 58.7% SWE-bench-Verified, 256K ctx, runs on 24 GB — purpose-built for agentic loops; the local bar \| open \|
	\| DeepSeek-V4 (Pro 1.6T/49B · Flash 284B/13B) \| 1M ctx, long-horizon planning, improved tool-call reliability (fewer malformed JSON) \| MIT \|
	\| GLM-5.1 (our base's sibling, 754B) \| 58.4 SWE-Pro, 8-hour autonomous execution — SOTA agentic \| MIT \|
	\| Kimi K2.6 Thinking \| 78.57 coding / 58.33 agentic — strongest open \| Modified-MIT \|
	\| Qwen 3.6 Plus \| 1M ctx, reliable tool-use over long sessions \| open \|
	\| frontier ref: Claude Opus 4.6 \| 50% task completion @ 14.5-hour horizon \| closed \|

	They compete on: context length · raw SWE-bench · tool-call reliability · long-horizon autonomy · speed.

	## Where we honestly can't win
	Raw size (we demolished GLM-5.2 → 99 GB / 3-bit), decode speed (11–14 tok/s), context (vs 1M), raw SWE-bench
	(70 % of experts pruned). Chasing those is a losing race against un-demolished 24 GB models.

	## Where we WIN — agentic reliability (the thing that actually breaks agents)
	The research names tool-call reliability as the #1 agentic differentiator (DeepSeek-V4's headline win was
	"fewer malformed JSON / partial calls"). We don't improve it — we guarantee it:
	- Constrained tool-JSON (#45): grammar-enforced tool-calls → zero malformed JSON, structurally impossible. (Field's best = "fewer"; ours = "none.")
	- Compiler-steered / verified decoding (#21, #24): every line type-checks as it's written (TS 0.3 ms · Py ~0 · Rust 34 ms).
	- Fabrication-proof `done` (#41): re-runs the original tests → can't hallucinate a pass.
	- Integrity layer: test-tamper guard, secret-scan (16 providers), scope enforcement, slopsquat guard.
	- 51-tool ReAct agent: trajectory compaction, stall detection, the verifier mesh (5 langs + SQL + Lean).

	Positioning: *"the local agentic coder that can't malform a tool-call, can't fake a test pass, and
	compiler-checks every line."* The others are bigger/faster; none ship the verify-everything stack. Reliability
	is a moat raw capability can't buy — and it's exactly what makes long agentic runs not collapse.

	## Adoptable from the field (CPU now → heal later)
	1. Tool-use cold-start SFT (AgentRL / ProRL pattern): query-formulation → tool-invocation → valid-JSON →
	result-read → recover-from-error. → generate agentic tool-use gold for the next soul heal.
	2. Long-horizon agentic data (128 turns / 131K ctx, Hierarchy-of-Groups PO): multi-step plan→edit→test→fix
	trajectories that don't lose the thread. Our agent is built for it — train + benchmark it.
	3. Tool-call self-correction (DeepSeek-V4's win): recover from a bad call. (Our constrained decoder prevents
	the bad call upstream — but the recovery skill still helps when external tools fail.)
	4. Atomic skills ("Scaling Coding Agents via Atomic Skills", 2604.05013): decompose tasks into reusable skills → our skill library.
	5. MCP / OpenAI-compat integration: the field plugs local models into Cline · OpenCode · Aider · Claude Code via
	OpenAI-compat + MCP. We already serve OpenAI-compat (`mlx_lm.server`) — document the one-line setup per agent so we're a drop-in.

	## Plan
	- NOW (CPU): (a) position the card around agentic reliability; (b) generate agentic tool-use + long-horizon +
	reliability gold (`heal/gold_agentic/`) for the next heal; (c) document the Cline/OpenCode/Aider/Claude-Code drop-in setup.
	- LATER (GPU, behind the factory + #59): heal the agentic gold into the soul; benchmark long-horizon
	(Terminal-Bench / FeatureBench / real-task, not the saturating SWE-bench); the #59 collapse fix keeps long agentic gens from degenerating.

	Sources: Qwen3-Coder-Next / DeepSeek-V4 / GLM-5.1 (MarkTechPost, kilo.ai, mindstudio 2026) · Agentic Tool Use 2604.00835 · AgentRL/ProRL 2603.18815 · Hierarchy-of-Groups PO 2602.22817 · Atomic Skills 2604.05013 · CWM 2510.02387 · OpenCode/Cline (morphllm 2026).