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
- 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"} ] }'
Upload research/agentic_coder_landscape.md with huggingface_hub
Browse files
research/agentic_coder_landscape.md
ADDED
|
@@ -0,0 +1,52 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# THE Local Agentic Coder — landscape + our path (June 2026)
|
| 2 |
+
|
| 3 |
+
Goal: make this model **THE** model for *local agentic coders*. This is the competitive scan + the honest
|
| 4 |
+
strategy that falls out of it.
|
| 5 |
+
|
| 6 |
+
## The field (local/open agentic coders, June 2026)
|
| 7 |
+
| Model | Agentic headline | License |
|
| 8 |
+
|---|---|---|
|
| 9 |
+
| **Qwen3-Coder-Next** | 58.7% SWE-bench-Verified, 256K ctx, **runs on 24 GB** — purpose-built for agentic loops; *the* local bar | open |
|
| 10 |
+
| **DeepSeek-V4** (Pro 1.6T/49B · Flash 284B/13B) | 1M ctx, long-horizon planning, **improved tool-call reliability** (fewer malformed JSON) | MIT |
|
| 11 |
+
| **GLM-5.1** (our base's sibling, 754B) | **58.4 SWE-Pro, 8-hour autonomous execution** — SOTA agentic | MIT |
|
| 12 |
+
| **Kimi K2.6 Thinking** | 78.57 coding / 58.33 agentic — strongest open | Modified-MIT |
|
| 13 |
+
| **Qwen 3.6 Plus** | 1M ctx, reliable tool-use over long sessions | open |
|
| 14 |
+
| frontier ref: **Claude Opus 4.6** | 50% task completion @ **14.5-hour** horizon | closed |
|
| 15 |
+
|
| 16 |
+
**They compete on:** context length · raw SWE-bench · **tool-call reliability** · long-horizon autonomy · speed.
|
| 17 |
+
|
| 18 |
+
## Where we honestly *can't* win
|
| 19 |
+
Raw size (we demolished GLM-5.2 → 99 GB / 3-bit), decode speed (11–14 tok/s), context (vs 1M), raw SWE-bench
|
| 20 |
+
(70 % of experts pruned). Chasing those is a losing race against un-demolished 24 GB models.
|
| 21 |
+
|
| 22 |
+
## Where we WIN — agentic *reliability* (the thing that actually breaks agents)
|
| 23 |
+
The research names **tool-call reliability as the #1 agentic differentiator** (DeepSeek-V4's headline win was
|
| 24 |
+
*"fewer malformed JSON / partial calls"*). We don't improve it — we **guarantee** it:
|
| 25 |
+
- **Constrained tool-JSON (#45):** grammar-enforced tool-calls → **zero** malformed JSON, structurally impossible. (Field's best = "fewer"; ours = "none.")
|
| 26 |
+
- **Compiler-steered / verified decoding (#21, #24):** every line type-checks *as it's written* (TS 0.3 ms · Py ~0 · Rust 34 ms).
|
| 27 |
+
- **Fabrication-proof `done` (#41):** re-runs the *original* tests → can't hallucinate a pass.
|
| 28 |
+
- **Integrity layer:** test-tamper guard, secret-scan (16 providers), scope enforcement, slopsquat guard.
|
| 29 |
+
- **51-tool ReAct agent:** trajectory compaction, stall detection, the verifier mesh (5 langs + SQL + Lean).
|
| 30 |
+
|
| 31 |
+
**Positioning:** *"the local agentic coder that can't malform a tool-call, can't fake a test pass, and
|
| 32 |
+
compiler-checks every line."* The others are bigger/faster; **none** ship the verify-everything stack. Reliability
|
| 33 |
+
is a moat raw capability can't buy — and it's exactly what makes long agentic runs not collapse.
|
| 34 |
+
|
| 35 |
+
## Adoptable from the field (CPU now → heal later)
|
| 36 |
+
1. **Tool-use cold-start SFT** (AgentRL / ProRL pattern): query-formulation → tool-invocation → valid-JSON →
|
| 37 |
+
result-read → recover-from-error. → generate **agentic tool-use gold** for the next soul heal.
|
| 38 |
+
2. **Long-horizon agentic data** (128 turns / 131K ctx, Hierarchy-of-Groups PO): multi-step plan→edit→test→fix
|
| 39 |
+
trajectories that *don't* lose the thread. Our agent is built for it — train + benchmark it.
|
| 40 |
+
3. **Tool-call self-correction** (DeepSeek-V4's win): recover from a bad call. (Our constrained decoder prevents
|
| 41 |
+
the bad call upstream — but the *recovery* skill still helps when external tools fail.)
|
| 42 |
+
4. **Atomic skills** ("Scaling Coding Agents via Atomic Skills", 2604.05013): decompose tasks into reusable skills → our skill library.
|
| 43 |
+
5. **MCP / OpenAI-compat integration:** the field plugs local models into Cline · OpenCode · Aider · Claude Code via
|
| 44 |
+
OpenAI-compat + MCP. We already serve OpenAI-compat (`mlx_lm.server`) — document the one-line setup per agent so we're a drop-in.
|
| 45 |
+
|
| 46 |
+
## Plan
|
| 47 |
+
- **NOW (CPU):** (a) position the card around **agentic reliability**; (b) generate agentic tool-use + long-horizon +
|
| 48 |
+
reliability gold (`heal/gold_agentic/`) for the next heal; (c) document the Cline/OpenCode/Aider/Claude-Code drop-in setup.
|
| 49 |
+
- **LATER (GPU, behind the factory + #59):** heal the agentic gold into the soul; benchmark long-horizon
|
| 50 |
+
(Terminal-Bench / FeatureBench / real-task, *not* the saturating SWE-bench); the #59 collapse fix keeps long agentic gens from degenerating.
|
| 51 |
+
|
| 52 |
+
*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).*
|