Instructions to use michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-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 michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-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 michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4

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 michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4

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 michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4

LM Studio
Jan
Ollama
How to use michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF with Ollama:
```
ollama run hf.co/michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4
```
Unsloth Desktop

How to use michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4

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": "michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4
```

Lemonade

How to use michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.Qwopus3.6-27B-v2-MTP-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Hermes Agent

How to use michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-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 michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4

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 michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4

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 "michaelw9999/Qwopus3.6-27B-v2-MTP-NVFP4-GGUF:NVFP4" \
  --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"

Qwopus3.6-27B-v2-MTP-NVFP4-GGUF / README.md

michaelw9999

Update README.md

dbcae70 verified 3 months ago

2.62 kB

base_model:
  - Jackrong/Qwopus3.6-27B-v2-MTP-GGUF
  - Qwen/Qwen3.6-27B
tags:
  - nvfp4
  - llama.cpp
  - qwopus
  - qwopus3.6-27B
  - qwen3.6-27B
  - mtp

This is the first release of the NVFP4 version of Jackrong's Qwopus3.6-27B-v2-MTP-GGUF.
Please note, I am not affiliated; this is my own quantization effort made with my experimental work-in-progress advanced-gguf-quantizer.

More evaluatlions will be underway, this page will be updated when those are complete.

Feedback on how to improve the quantizer/this quantization is appreciated.

For improved performance and quality, try my llama.cpp NVFP4-Repack with MXFP6 from:
https://github.com/michaelw9999/llama.cpp/tree/nvfp4repack_mxfp6_cuda
These branches are updated regularly.
NVFP4 repack preloads all tensors into a CUDA tile to boost speed. It is a tiny bit slower on first load, then provides ~10% prefill boost with a small reduction in token gen seen on larger models, and an increase on smaller models.
However, it also enables NVFP4 input scale, which boosts model correctness.

Initial performance results:

llama-bench on 5090:

qwen35 27B NVFP4 |  15.14 GiB |27.32 B | CUDA | pp512 | 5958.06 ± 6.46 |
qwen35 27B NVFP4 |  15.14 GiB |27.32 B | CUDA | tg128 | 73.72 ± 0.08 |

Perplexity/kld results against wiki2 test:

====== Perplexity statistics ======
Mean PPL(Q)                   :   6.959259 ±   0.045420
Mean PPL(base)                :   6.694312 ±   0.042968
Cor(ln(PPL(Q)), ln(PPL(base))):  98.93%
Mean ln(PPL(Q)/PPL(base))     :   0.038815 ±   0.000953
Mean PPL(Q)/PPL(base)         :   1.039578 ±   0.000991
Mean PPL(Q)-PPL(base)         :   0.264947 ±   0.006915

====== KL divergence statistics ======
Mean    KLD:   0.045311 ±   0.000650
Maximum KLD:  19.236860
99.9%   KLD:   2.298393
99.0%   KLD:   0.423422
95.0%   KLD:   0.135200
90.0%   KLD:   0.081522
Median  KLD:   0.017625
10.0%   KLD:   0.000521
 5.0%   KLD:   0.000152
 1.0%   KLD:   0.000024
 0.1%   KLD:   0.000004
Minimum KLD:  -0.000062

====== Token probability statistics ======
Mean    Δp: -0.450 ± 0.016 %
Maximum Δp: 99.955%
99.9%   Δp: 32.792%
99.0%   Δp: 13.999%
95.0%   Δp:  6.515%
90.0%   Δp:  3.788%
75.0%   Δp:  0.717%
Median  Δp: -0.012%
25.0%   Δp: -1.153%
10.0%   Δp: -4.684%
 5.0%   Δp: -8.120%
 1.0%   Δp: -20.878%
 0.1%   Δp: -55.713%
Minimum Δp: -99.221%
RMS Δp    :  6.022 ± 0.055 %
Same top p: 91.085 ± 0.074 %