Instructions to use xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF",
	filename="Qwopus3.6-35B-A3B-Coder-vram13-q2ex-imat.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use xero0000/Qwopus3.6-35B-A3B-Coder-vram13-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 xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF
# Run inference directly in the terminal:
llama cli -hf xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF
# Run inference directly in the terminal:
llama cli -hf xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF

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 xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF
# Run inference directly in the terminal:
./llama-cli -hf xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF

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 xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF

Use Docker

docker model run hf.co/xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF

LM Studio
Jan

vLLM

How to use xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF

Ollama
How to use xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF with Ollama:
```
ollama run hf.co/xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF
```

Unsloth Studio

How to use xero0000/Qwopus3.6-35B-A3B-Coder-vram13-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 xero0000/Qwopus3.6-35B-A3B-Coder-vram13-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 xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF to start chatting

How to use xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF

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": "xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use xero0000/Qwopus3.6-35B-A3B-Coder-vram13-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 xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF

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 xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF

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 "xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF" \
  --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 xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF with Docker Model Runner:
```
docker model run hf.co/xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF
```

Lemonade

How to use xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull xero0000/Qwopus3.6-35B-A3B-Coder-vram13-GGUF

Run and chat with the model

lemonade run user.Qwopus3.6-35B-A3B-Coder-vram13-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Qwopus3.6-35B-A3B-Coder — vram13: 151 t/s coding on 18 GB of VRAM (13.5 GB, imatrix, native MTP)

A SWE-bench-62 coding fine-tune of Qwen3.6-35B-A3B, quantized to fit entirely in 18 GB of VRAM with its native MTP head intact — 151 t/s code decode on a pair of mid-range gaming GPUs.

This is the coding-tuned sibling of Qwen3.6-35B-A3B-vram13-GGUF, and the best of the family: Qwopus ships with a trained multi-token-prediction head (most GGUFs drop it), so speculative decoding uses the head that was optimized for these exact weights — no transplant, best acceptance.

File

File	Size	ctx
`Qwopus3.6-35B-A3B-Coder-vram13-q2ex-imat.gguf`	13.53 GB	128 K (the ik MTP ceiling)

Measured performance (RTX 3060 Ti 8 GB + RTX 3080 10 GB, ik_llama.cpp, q4_0 KV, all-VRAM)

	decode	prefill @14.5 K
code (with `-mtp`)	151.5 t/s	966 t/s
prose	112 t/s	—
(same fine-tune, CPU-offloaded mixed quant, same rig)	101.5 t/s	602 t/s

Decode holds >100 t/s at 14.5 K tokens of depth. VRAM: ~6.6 + ~9.3 GB across the two cards.

Quality (perplexity, held-out corpus, ctx 512)

Quant	Size	PPL
Qwen3.6 base Q8_0 (family reference)	36.9 GB	2.4053
18 GB mixed-quant (CPU-offload recipe)	18.2 GB	2.4195
vram13 (this repo)	13.53 GB	2.4251 ± 0.061

Within noise of a quant 35 % larger — the best quality-per-byte of the all-VRAM builds we tested.

Recipe

Requantized from Q8_0 with a Qwopus-specific imatrix, using ik_llama.cpp llama-quantize --custom-q (required: mainline/other forks fail on the nextn layer with missing tensor blk.N.ssm_conv1d.weight):

Experts: q2_K middle layers 3–36, q3_K edge layers 0–2 & 37–39 (imatrix-guided)
Attention + SSM projections: q5_K
Shared expert, output, embeddings: q6_K
blk.40 MTP/nextn head: preserved
Base ftype q4_K so every custom rule fires

The principle: on small-active-expert MoE, expert weights dominate size but tolerate 2-bit with imatrix; everything on the every-token path stays 5–6-bit.

Running

Requires ik_llama.cpp for -mtp (and for loading the nextn arch reliably).

llama-server -m Qwopus3.6-35B-A3B-Coder-vram13-q2ex-imat.gguf \
  --jinja -fa on --cache-type-k q4_0 --cache-type-v q4_0 \
  --ctx-size 131072 -ngl 99 --tensor-split 44,56 --ubatch-size 256 --no-mmap \
  -mtp --ctx-size-draft 8192 --reasoning off

Notes: Qwopus is a thinking-off design — keep --reasoning off (also avoids empty-content responses). Single ≥16 GB GPU: drop --tensor-split. MTP output is verification-lossless; only speed varies with content (code accepts best).

Caveats

2-bit experts: expect slightly weaker edge-case reasoning than Q4-class quants — for maximum quality use a larger mixed quant with CPU offload.
128 K max context (ik clamps MTP models to leave room for the draft context); the non-MTP sibling repo runs 256 K.

Built on a Ryzen 5950X + 3060 Ti + 3080. Fine-tune by Jackrong (Apache-2.0), base model © Alibaba Cloud. Quantized with ik_llama.cpp tooling.