Instructions to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16

Use Docker

docker model run hf.co/jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16

LM Studio
Jan
Ollama
How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF with Ollama:
```
ollama run hf.co/jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16
```

Unsloth Studio

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF to start chatting

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16

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 jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16

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 "jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF with Docker Model Runner:
```
docker model run hf.co/jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16
```

Lemonade

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16

Run and chat with the model

lemonade run user.Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF-BF16

List all available models

lemonade list

jackasda211233 commited on Jun 29

Commit

2d6e4d6

verified ·

1 Parent(s): 42be896

Upload PATCHCODE_TESTING_PROCESS.html with huggingface_hub

Browse files

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PATCHCODE_TESTING_PROCESS.html +463 -39

PATCHCODE_TESTING_PROCESS.html CHANGED Viewed

@@ -1,38 +1,458 @@
-<!DOCTYPE html>
-<html lang="en"><head>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<title>Qwen3.6 AEON RYS PatchCode — Testing Process</title>
-<style>
-:root{--bg:#0d1117;--card:#161b22;--ink:#c9d1d9;--mut:#8b949e;--acc:#58a6ff;--bd:#30363d;--gw:#f85149;--gd:#3fb950}
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-.bnr a{color:#ffb3b3}
-</style>
 </head>
 <body>
-<main>
-<h1>Qwen3.6 AEON RYS PatchCode (merged_lam0.5): What We Actually Did</h1>
 <p>This is the longer, more casual write-up for the PatchCode upload candidate (internal project name <code>merged_lam0.5</code>).</p>
 <p>The clean model card stays short. This document is the full story: what we distilled, exactly how the dataset was built, how we tested it, why the early single-run scores fooled us, why we stopped trusting them, and why the upload candidate ended up being the plain <code>IQ4_NL</code> (reasoning-imatrix) merged GGUF rather than a heavier mixed-quant recipe.</p>
 <p>Related public guides:
@@ -71,7 +491,7 @@ li{margin:.25em 0}
 </code></pre>
 <p>The new work was not another RYS architecture pass (the architecture is unchanged and is documented in the layer-duplication guide). The new work was a behaviour distil layered on top of SignalLatch, then merged and quantized into the same practical Q4-class deployment lane.</p>
 <p>Public framing stays narrow:</p>
-<blockquote class="bnr">
 <p>This is a practical coding-agent / tool-use-oriented fine-tuned IQ4_NL variant of the SignalLatch release.</p>
 </blockquote>
 <p>It should not be framed as:
@@ -179,7 +599,7 @@ li{margin:.25em 0}
 </tbody>
 </table>
 <p>At λ=1.0 the adapter was net-neutral-to-harmful (one checkpoint fell <em>below</em> the un-adapted base). The mechanism: an over-loud LoRA delta pushes activations into regimes that hurt calibrated behaviour (preamble returns, over-claiming). λ=0.5 (effective alpha/r = 1.0) keeps the style direction but respects base calibration. So the merge was done at <strong>λ=0.5 onto SignalLatch (ckpt386-s010)</strong>, then exported to BF16 GGUF. (A future v2 could bake the good strength in by training at alpha=r=32, removing the inference-time knob.)</p>
-<p><img alt="Merge strength sweep — λ=0.5 wins on all three checkpoints; trained default λ=1.0 is over-applied." src="assets/lambda_sweep.png" /></p>
 <h2>Why the final testing moved to merged IQ4_NL</h2>
 <p>The key question was not "best adapter in BF16" — it was "what we would actually deploy". The deploy target was a merged GGUF, <code>IQ4_NL</code>, imatrix-quantized, on the custom ik-llama runtime (Jinja + DeepSeek reasoning format + flash attention + graph split, temp <code>0.7</code>).</p>
 <p>Live LoRA loading is not the production path for this release (the tested serving profile uses flash attention, which conflicts with live LoRA on this runtime). So the long-term path became: <strong>merge the adapter first, then export + quantize a full GGUF.</strong> That is why the upload is a merged GGUF, not an adapter.</p>
@@ -259,8 +679,8 @@ li{margin:.25em 0}
 <p>This is exactly how a 3-seed pass almost shipped the <em>weaker</em> model.</p>
 <h2>The ship decision</h2>
 <p>With build, discipline, and long-context all <strong>tied within noise</strong>, the decision fell to non-noise axes, where plain <code>IQ4_NL</code> wins all three:</p>
-<p><img alt="No candidate clears BOTH build and discipline (≥0.90) — promotion destroys discipline; precision does not fix build." src="assets/bothquest.png" /></p>
-<p><img alt="Ship scoreboard (5-seed): IQ4_NL ties the field within noise on build/long-context/discipline, and wins on size." src="assets/ship_scoreboard.png" /></p>
 <ul>
 <li><strong>smaller</strong> (16.6 G vs 20–29 G)</li>
 <li><strong>marginal long-context</strong> edge (0.975 vs 0.935–0.969)</li>
@@ -297,7 +717,11 @@ Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode.BF16.gguf        (57.6 GB — sourc
 <pre><code class="language-text">PatchCode IQ4_NL is a practical agentic-coder upgrade over the SignalLatch release.
 It is the selected default among the tested quants, tied with BF16 within noise —
 not a universal final answer.
-</code></pre>
-</main>
 </body>
 </html>

+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+  <title>PatchCode (agentic-coder) — Testing Process — Qwen3.6 AEON RYS Docs</title>
+  <meta name="description" content="Historical public record for the original Qwen3.6 AEON RYS SignalLatch ckpt386 s0.10 fine-tune, testing process, runtime setup, and first-release decision.">
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+    .step b {
+      display: block;
+      color: var(--green-dark);
+      margin-bottom: 8px;
+    }
+    .figure {
+      margin: 24px 0 0;
+      padding: 14px;
+      border: 1px solid var(--line);
+      border-radius: var(--radius);
+      background: var(--panel);
+      box-shadow: var(--soft-shadow);
+    }
+    .figure img {
+      display: block;
+      width: 100%;
+      height: auto;
+      border-radius: 6px;
+      background: #ffffff;
+    }
+    .caption {
+      margin-top: 10px;
+      color: var(--muted);
+      font-size: 0.9rem;
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+    .table-wrap {
+      overflow-x: auto;
+      border: 1px solid var(--line);
+      border-radius: var(--radius);
+      background: var(--panel);
+      box-shadow: var(--soft-shadow);
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+    }
+    table {
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+      min-width: 740px;
+      border-collapse: collapse;
+      font-size: 0.92rem;
+    }
+    th, td {
+      padding: 11px 12px;
+      border-bottom: 1px solid var(--line);
+      text-align: left;
+      vertical-align: top;
+    }
+    th {
+      background: var(--paper-2);
+      color: var(--ink);
+      font-weight: 900;
+    }
+    tr:last-child td { border-bottom: 0; }
+    .num { text-align: right; white-space: nowrap; }
+    .pass { color: var(--green-dark); font-weight: 800; }
+    .fail { color: var(--red); font-weight: 800; }
+    .soft { color: var(--muted); }
+    pre {
+      overflow-x: auto;
+      background: var(--code);
+      border: 1px solid var(--line);
+      border-radius: var(--radius);
+      padding: 16px;
+      color: var(--ink);
+      white-space: pre-wrap;
+      word-break: break-word;
+    }
+    .split-list {
+      display: grid;
+      grid-template-columns: repeat(2, minmax(0, 1fr));
+      gap: 16px;
+      margin-top: 18px;
+    }
+    ul {
+      padding-left: 20px;
+      margin: 0 0 16px;
+    }
+    li { margin: 6px 0; }
+    footer {
+      padding: 32px 0 52px;
+      border-top: 1px solid var(--line);
+      color: var(--muted);
+      font-size: 0.92rem;
+    }
+    @media (max-width: 920px) {
+      nav { align-items: flex-start; flex-direction: column; padding: 12px 0; }
+      nav .links { justify-content: flex-start; }
+      .hero { padding-top: 46px; }
+      .quick { grid-template-columns: repeat(2, minmax(0, 1fr)); }
+      .grid-2 { grid-template-columns: 1fr; }
+      .flow { grid-template-columns: 1fr; }
+      .cards, .split-list { grid-template-columns: 1fr; }
+    }
+    @media (max-width: 560px) {
+      .wrap { width: min(100% - 24px, 1180px); }
+      .quick { grid-template-columns: 1fr; }
+      .metric { min-height: auto; }
+      table { min-width: 680px; }
+    }
+  </style>
 </head>
 <body>
+  <header>
+    <div class="wrap">
+      <nav aria-label="Page navigation">
+        <a class="brand" href="../index.html">Qwen3.6 AEON RYS Docs</a>
+        <div class="links">
+          <a href="https://noonr48.github.io/qwen36-aeon-ik-llama/">Docs home</a>
+          <a href="https://noonr48.github.io/qwen36-aeon-ik-llama/qwen36-aeon-rys-signallatch/index.html">SignalLatch</a>
+          <a href="https://noonr48.github.io/qwen36-aeon-ik-llama/ckpt386-s010-testing-process/index.html">ckpt386 process</a>
+          <a href="https://noonr48.github.io/qwen36-aeon-ik-llama/signallatch-v1-1-production-process/index.html">v1.1 production</a>
+          <a href="https://noonr48.github.io/qwen36-aeon-ik-llama/signallatch-v1-1-all-results/index.html">v1.1 all results</a>
+          <a href="https://noonr48.github.io/qwen36-aeon-ik-llama/rys-layer-duplication-guide/">RYS arch guide</a>
+          <a href="https://noonr48.github.io/qwen36-aeon-ik-llama/qwen36-aeon-rys-15-20/index.html">AEON 15/20</a>
+        </div>
+      </nav>
+    </div>
+  </header>
+  <div class="hero">
+    <div class="wrap">
+      <div class="eyebrow">SignalLatch fine-tune record</div>
+      <h1>How the first ckpt386 s0.10 SignalLatch release was made, tested, and selected.</h1>
+      <p class="dek">This page is the self-contained public record for the original SignalLatch behavior fine-tune on top of Qwen3.6 AEON RYS 15/20. It documents the first public ckpt386 s0.10 IQ4_NL release, not the later v1.1 Lite/Plus mixed-quant selection.</p>
+      <div class="actions">
+        <a class="button primary" href="https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-SignalLatch-GGUF">SignalLatch on Hugging Face</a>
+        <a class="button" href="../signallatch-v1-1-production-process/index.html">Current v1.1 record</a>
+        <a class="button" href="../qwen36-aeon-rys-15-20/index.html">Base AEON RYS record</a>
+        <a class="button" href="../qwen36-aeon-rys-signallatch/index.html">Release overview</a>
+        <a class="button" href="https://github.com/noonr48/qwen36-aeon-ik-llama">Runtime fork</a>
+      </div>
+      <div class="quick" aria-label="Key release facts">
+        <div class="metric">
+          <strong>ckpt386</strong>
+          <span>Final one-epoch behavioral LoRA checkpoint used for the merge.</span>
+        </div>
+        <div class="metric">
+          <strong>s0.10</strong>
+          <span>Selected merge strength after deploy-format IQ4_NL sweeps and repeats.</span>
+        </div>
+        <div class="metric">
+          <strong>4/5</strong>
+          <span>First deploy-format s0.10 practical matrix pass count, mean 0.950.</span>
+        </div>
+        <div class="metric">
+          <strong>9/15</strong>
+          <span>Strict repeat-screen count used for strength selection, not a capability ceiling; 9/14 crash-adjusted.</span>
+        </div>
+      </div>
+    </div>
+  </div>
+  <main class="wrap"><div class="wrap"><h1>Qwen3.6 AEON RYS PatchCode (merged_lam0.5): What We Actually Did</h1>
 <p>This is the longer, more casual write-up for the PatchCode upload candidate (internal project name <code>merged_lam0.5</code>).</p>
 <p>The clean model card stays short. This document is the full story: what we distilled, exactly how the dataset was built, how we tested it, why the early single-run scores fooled us, why we stopped trusting them, and why the upload candidate ended up being the plain <code>IQ4_NL</code> (reasoning-imatrix) merged GGUF rather than a heavier mixed-quant recipe.</p>
 <p>Related public guides:
 </code></pre>
 <p>The new work was not another RYS architecture pass (the architecture is unchanged and is documented in the layer-duplication guide). The new work was a behaviour distil layered on top of SignalLatch, then merged and quantized into the same practical Q4-class deployment lane.</p>
 <p>Public framing stays narrow:</p>
+<blockquote>
 <p>This is a practical coding-agent / tool-use-oriented fine-tuned IQ4_NL variant of the SignalLatch release.</p>
 </blockquote>
 <p>It should not be framed as:
 </tbody>
 </table>
 <p>At λ=1.0 the adapter was net-neutral-to-harmful (one checkpoint fell <em>below</em> the un-adapted base). The mechanism: an over-loud LoRA delta pushes activations into regimes that hurt calibrated behaviour (preamble returns, over-claiming). λ=0.5 (effective alpha/r = 1.0) keeps the style direction but respects base calibration. So the merge was done at <strong>λ=0.5 onto SignalLatch (ckpt386-s010)</strong>, then exported to BF16 GGUF. (A future v2 could bake the good strength in by training at alpha=r=32, removing the inference-time knob.)</p>
+<p><img alt="Merge strength sweep — λ=0.5 wins on all three checkpoints; trained default λ=1.0 is over-applied." src="https://noonr48.github.io/qwen36-aeon-ik-llama/patchcode-testing-process/assets/lambda_sweep.png" /></p>
 <h2>Why the final testing moved to merged IQ4_NL</h2>
 <p>The key question was not "best adapter in BF16" — it was "what we would actually deploy". The deploy target was a merged GGUF, <code>IQ4_NL</code>, imatrix-quantized, on the custom ik-llama runtime (Jinja + DeepSeek reasoning format + flash attention + graph split, temp <code>0.7</code>).</p>
 <p>Live LoRA loading is not the production path for this release (the tested serving profile uses flash attention, which conflicts with live LoRA on this runtime). So the long-term path became: <strong>merge the adapter first, then export + quantize a full GGUF.</strong> That is why the upload is a merged GGUF, not an adapter.</p>
 <p>This is exactly how a 3-seed pass almost shipped the <em>weaker</em> model.</p>
 <h2>The ship decision</h2>
 <p>With build, discipline, and long-context all <strong>tied within noise</strong>, the decision fell to non-noise axes, where plain <code>IQ4_NL</code> wins all three:</p>
+<p><img alt="No candidate clears BOTH build and discipline (≥0.90) — promotion destroys discipline; precision does not fix build." src="https://noonr48.github.io/qwen36-aeon-ik-llama/patchcode-testing-process/assets/bothquest.png" /></p>
+<p><img alt="Ship scoreboard (5-seed): IQ4_NL ties the field within noise on build/long-context/discipline, and wins on size." src="https://noonr48.github.io/qwen36-aeon-ik-llama/patchcode-testing-process/assets/ship_scoreboard.png" /></p>
 <ul>
 <li><strong>smaller</strong> (16.6 G vs 20–29 G)</li>
 <li><strong>marginal long-context</strong> edge (0.975 vs 0.935–0.969)</li>
 <pre><code class="language-text">PatchCode IQ4_NL is a practical agentic-coder upgrade over the SignalLatch release.
 It is the selected default among the tested quants, tied with BF16 within noise —
 not a universal final answer.
+</code></pre></div></main>
+  <footer>
+    <div class="wrap">
+      Last updated: 2026-06-29. This page documents the PatchCode (agentic-coder) fine-tune and quant bake-off for the Qwen3.6 AEON RYS / SignalLatch line.</div>
+  </footer>
 </body>
 </html>