Instructions to use nerkyor/Qwen3.5-4B-GGUF-imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use nerkyor/Qwen3.5-4B-GGUF-imatrix 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 nerkyor/Qwen3.5-4B-GGUF-imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nerkyor/Qwen3.5-4B-GGUF-imatrix:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nerkyor/Qwen3.5-4B-GGUF-imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nerkyor/Qwen3.5-4B-GGUF-imatrix: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 nerkyor/Qwen3.5-4B-GGUF-imatrix:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nerkyor/Qwen3.5-4B-GGUF-imatrix: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 nerkyor/Qwen3.5-4B-GGUF-imatrix:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nerkyor/Qwen3.5-4B-GGUF-imatrix:Q4_K_M

Use Docker

docker model run hf.co/nerkyor/Qwen3.5-4B-GGUF-imatrix:Q4_K_M

LM Studio
Jan

vLLM

How to use nerkyor/Qwen3.5-4B-GGUF-imatrix with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nerkyor/Qwen3.5-4B-GGUF-imatrix"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nerkyor/Qwen3.5-4B-GGUF-imatrix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nerkyor/Qwen3.5-4B-GGUF-imatrix:Q4_K_M

Ollama
How to use nerkyor/Qwen3.5-4B-GGUF-imatrix with Ollama:
```
ollama run hf.co/nerkyor/Qwen3.5-4B-GGUF-imatrix:Q4_K_M
```
Unsloth Desktop

How to use nerkyor/Qwen3.5-4B-GGUF-imatrix with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nerkyor/Qwen3.5-4B-GGUF-imatrix: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": "nerkyor/Qwen3.5-4B-GGUF-imatrix:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use nerkyor/Qwen3.5-4B-GGUF-imatrix with Docker Model Runner:
```
docker model run hf.co/nerkyor/Qwen3.5-4B-GGUF-imatrix:Q4_K_M
```

Lemonade

How to use nerkyor/Qwen3.5-4B-GGUF-imatrix with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nerkyor/Qwen3.5-4B-GGUF-imatrix:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-4B-GGUF-imatrix-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use nerkyor/Qwen3.5-4B-GGUF-imatrix with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nerkyor/Qwen3.5-4B-GGUF-imatrix: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 nerkyor/Qwen3.5-4B-GGUF-imatrix:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nerkyor/Qwen3.5-4B-GGUF-imatrix with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nerkyor/Qwen3.5-4B-GGUF-imatrix: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 "nerkyor/Qwen3.5-4B-GGUF-imatrix: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"

Qwen3.5-4B Q4_K_M (imatrix) - Lynn calibration

This repository ships an imatrix-calibrated Q4_K_M GGUF of the official Qwen/Qwen3.5-4B BF16 weights, built on DGX Spark (GB10, sm_121) by the Lynn team.

This is a pure quantization of the upstream model. It is not a distillation and not a Lynn-native NVFP4/W4A8 checkpoint.

Files

File	Size	SHA256	Role
`Qwen3.5-4B-Q4_K_M-imatrix.gguf`	2.6 GB	`7abaf02bbe25c608deb308db526766f761ad4fb85c512a69ff36520c4b304b23`	llama.cpp / Ollama / LM Studio GGUF weights
`Qwen3.5-4B.imatrix`	3.5 MB	`863a93c58a14925b58303a369d9bb155411b40d52fde121f195eaf7a6691f07c`	imatrix calibration data used for quantization

Build Details

Source weights: Qwen/Qwen3.5-4B official BF16
Converter: llama.cpp convert_hf_to_gguf.py --outtype f16
Calibration: Lynn Chinese + English + code mix, 256 chunks, 512 ctx
Quantizer: llama-quantize --imatrix Qwen3.5-4B.imatrix ... Q4_K_M
Built on: DGX Spark (GB10, sm_121), 2026-05-24

Evaluation

V8/V9, MMLU500, and GPQA Diamond thinking-on evaluations are running on Spark. Those artifacts and scores will be added in a follow-up update.

Run

llama-server \
  -m Qwen3.5-4B-Q4_K_M-imatrix.gguf \
  --host 0.0.0.0 --port 18099 \
  --ctx-size 32768 --n-gpu-layers 999 \
  --jinja --reasoning on

The GGUF embeds the upstream Qwen3.5 chat template, including thinking-mode support via chat_template_kwargs.enable_thinking.

License

Apache 2.0, inherited from Qwen/Qwen3.5-4B.

Downloads last month: 87

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for nerkyor/Qwen3.5-4B-GGUF-imatrix

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Qwen/Qwen3.5-4B-Base

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Quantized

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