Instructions to use mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Qwopus3.6-35B-A3B-Coder-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 mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M

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 mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M

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 mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M
```
Unsloth Desktop

How to use mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M

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": "mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwopus3.6-35B-A3B-Coder-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mradermacher/Qwopus3.6-35B-A3B-Coder-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 mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M

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 mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M

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 "mradermacher/Qwopus3.6-35B-A3B-Coder-GGUF:Q4_K_M" \
  --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"

Qwopus3.6-35B-A3B-Coder-GGUF / README.md

mradermacher

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metadata

base_model: Jackrong/Qwopus3.6-35B-A3B-Coder
language:
  - en
  - zh
  - es
  - ru
  - ja
library_name: transformers
license: apache-2.0
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - qwen3_6
  - moe
  - coder
  - agent
  - tool-use
  - function-calling
  - thinking-off
  - long-context
  - lora
  - sft
  - logic

About

static quants of https://huggingface.co/Jackrong/Qwopus3.6-35B-A3B-Coder

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwopus3.6-35B-A3B-Coder-i1-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	mmproj-Q8_0	0.7	multi-modal supplement
GGUF	mmproj-f16	1.0	multi-modal supplement
GGUF	Q2_K	13.3
GGUF	Q3_K_S	15.6
GGUF	Q3_K_M	17.3	lower quality
GGUF	Q3_K_L	18.7
GGUF	IQ4_XS	19.5
GGUF	Q4_K_S	20.5	fast, recommended
GGUF	Q4_K_M	21.8	fast, recommended
GGUF	Q5_K_S	24.7
GGUF	Q5_K_M	25.4
GGUF	Q6_K	29.3	very good quality
GGUF	Q8_0	37.9	fast, best quality

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.