Instructions to use wepiqx/RINIQ-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 wepiqx/RINIQ-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 wepiqx/RINIQ-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf wepiqx/RINIQ-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf wepiqx/RINIQ-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf wepiqx/RINIQ-GGUF:F16

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 wepiqx/RINIQ-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf wepiqx/RINIQ-GGUF:F16

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 wepiqx/RINIQ-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wepiqx/RINIQ-GGUF:F16

Use Docker

docker model run hf.co/wepiqx/RINIQ-GGUF:F16

Llama new
LM Studio
Jan
Ollama
How to use wepiqx/RINIQ-GGUF with Ollama:
```
ollama run hf.co/wepiqx/RINIQ-GGUF:F16
```
Unsloth Desktop

How to use wepiqx/RINIQ-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wepiqx/RINIQ-GGUF:F16

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": "wepiqx/RINIQ-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use wepiqx/RINIQ-GGUF with Docker Model Runner:
```
docker model run hf.co/wepiqx/RINIQ-GGUF:F16
```

Lemonade

How to use wepiqx/RINIQ-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wepiqx/RINIQ-GGUF:F16

Run and chat with the model

lemonade run user.RINIQ-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use wepiqx/RINIQ-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 wepiqx/RINIQ-GGUF:F16

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 wepiqx/RINIQ-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use wepiqx/RINIQ-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wepiqx/RINIQ-GGUF:F16

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 "wepiqx/RINIQ-GGUF:F16" \
  --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"

RINIQ-MERNIK-GGUF — tri-fusion 9B quants

Finetune of Qwen/Qwen3.5-9B by layer surgery (all donors descend from the same Qwen3.5-9B ancestor).

RINIQ is a new finetune built by layer surgery, quantized by the MERNIK engine. Three Qwen3.5-9B donors, one 427-tensor trunk (Ornith's MTP head excluded), per-block donor maps — no averaging of divergent blocks, ever. Code and protocol: wepiqx/MERNIK (full ledger in FUSION.md there).

Vision — natively multimodal family

Qwen3.5-9B is a natively multimodal family (early-fusion vision+language, jointly trained — not a bolted-on encoder). This repo ships mmproj-F16.gguf (stock Qwen3.5-9B vision projector, ~876 MB) next to the text quants: load both for image/video input, omit --mmproj for text-only.

llama-server -m RINIQ-M2-MERNIK-5100-Q38.gguf --mmproj mmproj-F16.gguf --port 28082 -ngl 99 -c 8192 --jinja

Note (MERNIK honesty): the HE/PPL verdicts below certify the text trunk only. Vision quality on fused RINIQ blocks is UNVERIFIED — no vision bench yet.

❤ If RINIQ runs on your hardware, consider sponsoring @wepiqx — consumer-GPU research, every scar public.

Donors

Ox = OxCoder-9B (agentic code distillate, HE 90.24%) — base + globals
Neo = NeoHorse-1-9B (thinking model) — donor of blk 31
Orn = Ornith-1.5-9B-MTP trunk (GDN hybrid) — donor of mid/late blocks

Verdicts (slow ring: HumanEval pass@1, temp 1.0 / top_p 0.95 / top_k 20)

Build	Size	PPL (ctx1024)	GPQA-rec	HE pass@1	HE+	Empties
RINIQ-M2-MERNIK-5100-Q38 👑	5.0 GB	7.6130	47.47%	92.07% (151/164)	87.8	8 — own-qwen38 lens: lost everything, won verdict
RINIQ-M2-MERNIK-5100	5.0 GB	7.5819	50.00%	91.46% (150/164)	87.2	6
RINIQ-M1-MERNIK-5100	5.0 GB	7.6242	51.01%	90.85% (149/164)	85.4	7
RINIQ-M3-MERNIK-5100	5.0 GB	7.7111	50.00%	86.59% (142/164)	83.5	13 — soup buried
RINIQ-M4a-MERNIK-5100	5.0 GB	7.5898	51.01%	91.46% (150/164)	87.2	4 — ties M2 + M1, best decisiveness
RINIQ-M4b-MERNIK-5100	5.0 GB	7.5743	48.48%	89.63% (147/164)	84.1	4 — Orn-25 stronger than Ox-25 by 1.8pp
RINIQ-M4c-MERNIK-5100	5.0 GB	7.5863	47.98%	90.85% (149/164)	85.4	6 — seam not poisonous
RINIQ-M2-SMAPE-6500	6.8 GB	7.5730	48.48%	89.63% (147/164)	83.5	5 — wrong utility, scar
RINIQ-M2-MERNIK-6500	6.8 GB	7.5263	48.48%	91.46% (150/164)	85.4	8 — ties 5100: ceiling
RINIQ-M5-MERNIK-5100	5.0 GB	7.6587	52.02%	91.46% (150/164)	84.8	7 — kitchen sink
RINIQ-M6-MERNIK-5100	5.6 GB	8.5137	46.97%	83.54% (137/164)	79.9 (−3.6)	23 — BASE RULES
RINIQ-M7-MERNIK-5100	5.0 GB	7.6033	49.49%	88.41% (145/164)	86.0 (−2.4)	11 — first auto-fused
RINIQ-M2-MERNIK-5100-MIX	5.0 GB	7.6001	48.99%	89.63% (147/164)	85.4 (−3.6)	6 — novel geometry
RINIQ-M2-RMSE-Q38-6500	6.8 GB	7.5411	50.00%	88.41% (145/164)	82.9 (−4.9)	6 — takes GPQA, loses HE by 3pp
Ox-MSE-6500 (parent)	6.8 GB	7.5125	49.5%	90.24% (148/164)	—	4–5
Ox-SMAPE-5100	5.0 GB	7.5670	49.0%	88.41% (145/164)	—	4–5

Recipes: M1 = Ox + Orn blks 15,19,23,27 (weight compass) + Neo blk 31. M2 = Ox + Orn blks 24,25,26 (imatrix compass) + Neo blk 31. M3 = M1 + blks 0–8 weight-averaged Ox+Neo (soup backbone) — soup buried (−4.9pp vs M2, honest scar in FUSION.md).

M4a/b/c (finale slate, verdicts in): M4a = M2 + blk 16←Orn; M4b = M2 with blk 25→Ox (one-block A/B on Orn-25); M4c = M2 + blk 30←Orn (seam with Neo-31).

External reference: JackOD-9B-Coder (different harness!)

Corporate 4-way omnimerge_v2 over the same Qwen3.5-9B ancestor (DeltaCoder 0.55 / Qwopus 0.30 / Ornith 0.15). Their numbers are quoted from their README — greedy temp 0.0, mixed banks (their own † admits base cells come from an earlier bank), NOT our protocol. Same-harness duel (LiveCodeBench v6 55 hard, custom_evaluator on our generations) is queued and will replace this table.

Build	HE	HE+	LCB v6 55 hard
RINIQ-M2-MERNIK-5100-Q38 (ours)	92.07	87.8	⏳ duel queued
JackOD-9B-Coder (Q6_K, theirs)	88.41	82.32	78.18

We lead on their reported HE/HE+; they lead where we haven't measured yet. No conclusions until one harness scores both.

Status

Series CLOSED: crown Q38 (92.07), no 93%. Files land as decided. Quants land here as verdicts complete. Naming: RINIQ-M<i>-MERNIK-<size>[-Q38].gguf. No file is uploaded before its HE verdict unless marked UNVERIFIED.