Instructions to use steven0226/Qwen3.5-9B-GGUF-Quant-Lab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use steven0226/Qwen3.5-9B-GGUF-Quant-Lab 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 steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf steven0226/Qwen3.5-9B-GGUF-Quant-Lab: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 steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf steven0226/Qwen3.5-9B-GGUF-Quant-Lab: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 steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M

Use Docker

docker model run hf.co/steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M

LM Studio
Jan

vLLM

How to use steven0226/Qwen3.5-9B-GGUF-Quant-Lab with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "steven0226/Qwen3.5-9B-GGUF-Quant-Lab"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "steven0226/Qwen3.5-9B-GGUF-Quant-Lab",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M

Ollama
How to use steven0226/Qwen3.5-9B-GGUF-Quant-Lab with Ollama:
```
ollama run hf.co/steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M
```

Unsloth Studio

How to use steven0226/Qwen3.5-9B-GGUF-Quant-Lab 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 steven0226/Qwen3.5-9B-GGUF-Quant-Lab 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 steven0226/Qwen3.5-9B-GGUF-Quant-Lab to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for steven0226/Qwen3.5-9B-GGUF-Quant-Lab to start chatting

How to use steven0226/Qwen3.5-9B-GGUF-Quant-Lab with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M

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": "steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use steven0226/Qwen3.5-9B-GGUF-Quant-Lab with Docker Model Runner:
```
docker model run hf.co/steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M
```

Lemonade

How to use steven0226/Qwen3.5-9B-GGUF-Quant-Lab with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-9B-GGUF-Quant-Lab-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use steven0226/Qwen3.5-9B-GGUF-Quant-Lab with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf steven0226/Qwen3.5-9B-GGUF-Quant-Lab: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 steven0226/Qwen3.5-9B-GGUF-Quant-Lab:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use steven0226/Qwen3.5-9B-GGUF-Quant-Lab with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf steven0226/Qwen3.5-9B-GGUF-Quant-Lab: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 "steven0226/Qwen3.5-9B-GGUF-Quant-Lab: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"

Qwen3.5-9B GGUF Quant Lab

Reproducible llama.cpp GGUF quantization ladder for Qwen/Qwen3.5-9B, evaluated on English and Traditional Chinese perplexity, TMMLU+, throughput, and VRAM.

This repository contains the text-only language model. It does not include a vision projector (mmproj) and cannot provide the original model's image-input capability.

Recommended default

IQ4_XS-imatrix is the measured sweet spot on the RTX 4090 test system: 4.840 GiB, English/Traditional-Chinese PPL increases of +1.50%/+1.95% from F16, and a TMMLU+ change of -1.0 percentage point. Choose Q4_K_M-imatrix when you prefer slightly stronger PPL retention at a larger 5.243 GiB size.

Quantization and evaluation

Hardware: RTX 4090 24 GiB, Intel i7-13700, WSL2 Ubuntu, CUDA 13.1, llama.cpp b10031. Performance is hardware-specific. PPL is a next-token likelihood measure, not an instruction-following or safety score. TMMLU+ may have appeared in the base model's training data; here it is used only for relative quantization degradation because every quantization receives the identical fixed 500-question sample.

Quant	GiB	English PPL	繁中 PPL	TMMLU+	pp512 tok/s	tg128 tok/s
F16	16.690	8.0111	10.0015	68.8%	7589	212
Q8_0	8.873	8.0190	9.9952	69.2%	9038	85
Q6_K	6.854	8.0160	10.0306	69.4%	7545	99
Q5_K_M	6.024	8.0591	10.1387	68.2%	8704	114
Q4_K_M	5.243	8.1110	10.2830	68.4%	8881	123
Q3_K_M	4.306	9.0446	11.1233	63.0%	8140	141
Q2_K	3.564	100.8741	44.6410	28.6%	7412	159
Q4_K_M-imatrix	5.243	8.0865	10.1508	68.2%	8777	101
IQ4_XS-imatrix	4.840	8.1311	10.1969	67.8%	9539	135

Full methodology, paired bootstrap CIs, Q2_K regression examples, figures, and reproducibility metadata are in EVAL_REPORT.md.

llama.cpp

./llama-cli -m Qwen3.5-9B-IQ4_XS-imatrix.gguf -ngl 99 --jinja --reasoning off -p "請用繁體中文介紹 GGUF 量化。"

Ollama

Download a GGUF file and the included Modelfile, then run:

ollama create qwen35-9b-quant-lab -f Modelfile
ollama run qwen35-9b-quant-lab

The supplied Modelfile points at Qwen3.5-9B-IQ4_XS-imatrix.gguf. Change FROM to select another local GGUF.

LM Studio

Download one .gguf file, open LM Studio's local model picker, and select the file. This is a Qwen3.5 text-only model; keep the embedded GGUF chat template enabled and disable reasoning if you want the same evaluation-style behavior.

Attribution and license

Base model: Qwen/Qwen3.5-9B at revision c202236235762e1c871ad0ccb60c8ee5ba337b9a. The original model and these quantized derivative weights are distributed under Apache-2.0; see LICENSE. Project scripts and documentation are MIT-licensed. This repository was staged for steven0226/Qwen3.5-9B-GGUF-Quant-Lab.

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GGUF

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Architecture

qwen35

Hardware compatibility

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