Instructions to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Use Docker

docker model run hf.co/tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

LM Studio
Jan

vLLM

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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": "tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Ollama
How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with Ollama:
```
ollama run hf.co/tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
```
Unsloth Desktop

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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": "tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with Docker Model Runner:
```
docker model run hf.co/tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
```

Lemonade

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Run and chat with the model

lemonade run user.Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF-IQ3_XXS

List all available models

lemonade list

Hermes Agent

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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 "tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS" \
  --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"

tooltd commited on Aug 22

Commit

33d23e9

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1 Parent(s): dff0bf2

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@@ -12,23 +12,19 @@ tags:
 - 16GB
 ---
-# Qwen3.8-27B (GGUF — ZB-ZipBrain Quantization)
-This repository provides GGUF quantizations for **Qwen3.8-27B** optimized using **ZB-ZipBrain**, a layer-wise quantization profiling and allocation method.
 ## 1. Overview & Method: ZB-ZipBrain
 **ZB-ZipBrain** is an automated layer-allocation approach that dynamically profiles model layers and mixes **K-quants** and **IQ-quants** based on layer sensitivity and importance matrices.
 > I developed and tested this method alongside AI over the past three days. It was created purely for research purposes 😊
 ### Key Objectives:
 * **Selective Bit-rate Allocation:** Assigns higher precision to sensitive layers and compact IQ-quants to more resilient weights.
 * **Balanced Efficiency:** Maintains low perplexity (PPL) and minimal Kullback-Leibler (KL) Divergence relative to the BF16 baseline while achieving target file sizes / bits-per-weight (bpw).
 ## 2. Benchmark & Evaluation Results
 All models were evaluated against the **BF16 baseline** (`Mean PPL = 6.950493`) using standard Perplexity (PPL) and KL Divergence metrics.
 ### Comprehensive Comparison Table

 - 16GB
 ---
+# Qwen3.8-27B (GGUF target for 16GB VRAM)
+- This repository provides GGUF quantizations for **Qwen3.8-27B** optimized using **ZB-ZipBrain**, a layer-wise quantization profiling and allocation method.
+- Focusing on levels starting at 4.0 bpw, suitable for a 16GB VRAM budget.
 ## 1. Overview & Method: ZB-ZipBrain
 **ZB-ZipBrain** is an automated layer-allocation approach that dynamically profiles model layers and mixes **K-quants** and **IQ-quants** based on layer sensitivity and importance matrices.
 > I developed and tested this method alongside AI over the past three days. It was created purely for research purposes 😊
 ### Key Objectives:
 * **Selective Bit-rate Allocation:** Assigns higher precision to sensitive layers and compact IQ-quants to more resilient weights.
 * **Balanced Efficiency:** Maintains low perplexity (PPL) and minimal Kullback-Leibler (KL) Divergence relative to the BF16 baseline while achieving target file sizes / bits-per-weight (bpw).
 ## 2. Benchmark & Evaluation Results
 All models were evaluated against the **BF16 baseline** (`Mean PPL = 6.950493`) using standard Perplexity (PPL) and KL Divergence metrics.
 ### Comprehensive Comparison Table