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 Desktop

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 @earendil-works/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

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

Hermes Agent

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

OpenClaw

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"

jackasda211233 commited on Jun 29

Commit

fa52036

verified ·

1 Parent(s): cdfea55

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -84,6 +84,8 @@ Use the single-file `BF16` GGUF only if you want to explore the merged model dir
 - not a general chat benchmark claim
 - not a stock `llama.cpp` / `vLLM` release
 ## What changed vs the SignalLatch release
 The previous SignalLatch file is the base deployment target this is meant to improve:
@@ -142,7 +144,7 @@ The bake-off served the model on a 4-GPU pool (1× RTX 5090 + 3× RTX 3090) with
   --reasoning-budget 0
 ```
-Sampling temp varies by gate: the exact-answer SignalLatch gates were scored at `--temp 0.0` for determinism; the coding-agent build matrix ran at `--temp 0.7`.
 **Single-GPU deployment:**
@@ -156,7 +158,7 @@ On one visible GPU, swap graph split for `-sm none`:
   -np 1 \
   -fa on \
   -sm none \
-  --temp 0.7 \
   --jinja \
   --reasoning-format deepseek \
   --reasoning-budget 0
@@ -176,7 +178,7 @@ On one visible GPU, swap graph split for `-sm none`:
   -sm none \
   -ctk f16 \
   -ctv f16 \
-  --temp 0.7 \
   --jinja \
   --reasoning-format deepseek \
   --reasoning-budget 0
@@ -241,8 +243,6 @@ A 15-case behaviour rubric (action-first style + coding discipline + held-out ge
 PatchCode scores higher while emitting ~⅓ the tokens — the base rambled (~311 tokens of hedging preamble), PatchCode was terse and on-target. λ=0.5 is the sweet spot: higher strengths also got terse but fell *below* the base (an over-loud LoRA delta hurting calibrated behaviour). Caveat: a behaviour rubric, not a multi-turn agent turn-count; single-temperature, small per-category N.
-![Merge-strength sweep — λ=0.5 peaks on every checkpoint; the trained default (λ=1.0) is over-applied and falls below the base.](docs/assets/lambda_sweep.png)
 ### Why there is no Q8 release
 A near-lossless `Q8_0` was built and tested 5-seed head-to-head against the shipped IQ4_NL (table 2). It showed **no beyond-noise edge on any axis** and is ~2× the size — near-lossless precision buys nothing measurable here because the build is ceiling-limited and noisy, not precision-limited. Attention-promotion mixed recipes (c76 and the overnight precision×promotion matrix) were tested for the same reason and ruled out: promotion destroyed discipline for no build gain. Only `IQ4_NL` and `BF16` are released.

 - not a general chat benchmark claim
 - not a stock `llama.cpp` / `vLLM` release
+![Merge-strength sweep — λ=0.5 peaks on every checkpoint; the trained default (λ=1.0) is over-applied and falls below the base.](docs/assets/lambda_sweep.png)
 ## What changed vs the SignalLatch release
 The previous SignalLatch file is the base deployment target this is meant to improve:
   --reasoning-budget 0
 ```
+Sampling temp: the KritaLite build discriminator ran greedy at `--temp 0.0`; the discipline rubric at `0.2`. An agentic temp sweep (`0.0 / 0.3 / 0.6 / 0.9`) found PatchCode robust across `0.0–0.6` (all converge), most turn-efficient at `0.6`, degrading at `0.9` — so `--temp 0.6` is the recommended default below (or `--temp 0.0` greedy for deterministic single-shot coding).
 **Single-GPU deployment:**
   -np 1 \
   -fa on \
   -sm none \
+  --temp 0.6 \
   --jinja \
   --reasoning-format deepseek \
   --reasoning-budget 0
   -sm none \
   -ctk f16 \
   -ctv f16 \
+  --temp 0.6 \
   --jinja \
   --reasoning-format deepseek \
   --reasoning-budget 0
 PatchCode scores higher while emitting ~⅓ the tokens — the base rambled (~311 tokens of hedging preamble), PatchCode was terse and on-target. λ=0.5 is the sweet spot: higher strengths also got terse but fell *below* the base (an over-loud LoRA delta hurting calibrated behaviour). Caveat: a behaviour rubric, not a multi-turn agent turn-count; single-temperature, small per-category N.
 ### Why there is no Q8 release
 A near-lossless `Q8_0` was built and tested 5-seed head-to-head against the shipped IQ4_NL (table 2). It showed **no beyond-noise edge on any axis** and is ~2× the size — near-lossless precision buys nothing measurable here because the build is ceiling-limited and noisy, not precision-limited. Attention-promotion mixed recipes (c76 and the overnight precision×promotion matrix) were tested for the same reason and ruled out: promotion destroyed discipline for no build gain. Only `IQ4_NL` and `BF16` are released.