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 20

Commit

0d52ee2

verified ·

1 Parent(s): ac02edc

Update README.md

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README.md +29 -18

README.md CHANGED Viewed

@@ -20,8 +20,7 @@ This repository provides GGUF quantizations for **Qwen3.8-27B** optimized using
 **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,
-> so feel free to use or modify it however you like. If you find it useful, don't forget to drop a like! 👍 😊
 ### Key Objectives:
@@ -34,6 +33,8 @@ All models were evaluated against the **BF16 baseline** (`Mean PPL = 6.950493`)
 ### Comprehensive Comparison Table
 | Rank | Model / File Name | Level / Source | Quant Type | Size (GB) | Mean PPL | Δ PPL | Mean KLD | Same Top-p (%) | KLD 99% |
 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
 | **1** | `Qwen3.8-27B-UD-Q8_K_XL` | unsloth UD2 | UD-Q8_K_XL | 29.30 | 6.953800 | +0.003500 | 0.000850 | 98.970% | — |
@@ -42,20 +43,32 @@ All models were evaluated against the **BF16 baseline** (`Mean PPL = 6.950493`)
 | **4** | `Qwen3.8-27B-Q5_K_M` | unsloth UD2 | Q5_K_M | 18.47 | 6.974200 | +0.023900 | 0.006220 | 96.700% | — |
 | 5 | `Qwen3.8-27B-UD-Q4_K_XL` | unsloth UD2 | Q4_K_XL | 16.69 | 6.979220 | +0.028728 | 0.008606 | 96.091% | 0.091099 |
 | **6** | `Qwen3.8-27B-ZB4.97-GOD-IQ4_XS` | **ZB-GOD** | IQ4_XS | **15.82** | **7.004243** | **+0.053751** | **0.012249** | **95.337%** | **0.117617** |
-| **7** | `Qwen3.8-27B-ZB4.65-PRO-IQ4_XS` | **ZB-PRO** | IQ4_XS | **14.81** | **7.017278** | **+0.066786** | **0.015466** | **94.766%** | **0.150252** |
-| **8** | `Qwen3.8-27B-Q4_K_M` | unsloth UD2 | Q4_K_M | 15.93 | 6.956100 | +0.005800 | 0.015490 | 94.650% | — |
-| **9** | `Qwen3.8-27B-ZB4.60-PRO-IQ4_XS` | **ZB-PRO** | IQ4_XS | **14.65** | **7.030895** | **+0.080402** | **0.016162** | **94.668%** | **0.159115** |
-| **10** | `Qwen3.8-27B-ZB4.55-PRO-IQ4_XS` | **ZB-PRO** | IQ4_XS | **14.49** | **7.032263** | **+0.081771** | **0.016647** | **94.613%** | **0.161016** |
-| 11 | `Qwen3.8-27B-IQ4_NL` | bartowski | IQ4_NL | 15.20 | 7.006472 | +0.055980 | 0.018427 | 94.230% | 0.190168 |
-| 12 | `Qwen3.8-27B-IQ4_XS` | unsloth UD2 | IQ4_XS | 14.63 | 7.012695 | +0.062202 | 0.018652 | 94.270% | 0.194338 |
-| **13** | `Qwen3.8-27B-ZB4.48-STD-IQ4_XS` | **ZB-STD** | IQ4_XS | **14.26** | **7.050096** | **+0.099604** | **0.018892** | **94.199%** | **0.196005** |
-| 14 | `Qwen3.8-27B-Q4_K_S` | unsloth UD2 | Q4_K_S | 15.01 | 6.966826 | +0.016334 | 0.018921 | 94.235% | 0.192749 |
-| 15 | `Qwen3.8-27B-IQ4_XS-i1` | mradermacher | IQ4_XS | 14.26 | 7.012810 | +0.062318 | 0.019271 | 94.141% | 0.197891 |
-| 16 | `Qwen3.8-27B-Q4_K_S-i1` | mradermacher | Q4_K_S | 14.74 | 6.989551 | +0.039059 | 0.019805 | 93.996% | 0.204143 |
-| **17** | `Qwen3.8-27B-ZB4.36-STD-IQ4_XS` | **ZB-STD** | IQ4_XS | **13.88** | **7.054811** | **+0.104319** | **0.020556** | **93.951%** | **0.206689** |
-| 18 | `Qwen3.8-27B-Q4_0-AutoRound-Code` | webhie | Q4_0 | 14.64 | 7.067142 | +0.116650 | 0.026586 | 92.970% | 0.271619 |
-| **19** | `Qwen3.8-27B-ZB4.14-MIN-IQ4_XS` | **ZB-MIN** | IQ4_XS | **13.19** | **7.045689** | **+0.095196** | **0.029334** | **92.799%** | **0.294996** |
-| 20 | `Qwen3.8-27B-IQ4_XS-Smaller_3.96` | jrell | IQ4_XS | 12.61 | 7.252766 | +0.302274 | 0.055499 | 90.090% | 0.551972 |
 ## 3. ZB-ZipBrain Tiers & Recommendations
@@ -64,8 +77,6 @@ All models were evaluated against the **BF16 baseline** (`Mean PPL = 6.950493`)
 * **ZB-STD :** Standard. Similar to other standard IQ4_XS models currently available.
 * **ZB-MIN :** Minimal. Optimal footprint for tight 16 GB memory setups, allowing headroom for longer context windows
-**Note: These two missing files will be uploaded soon: Qwen3.8-27B-ZB4.97-GOD-IQ4_XS, Qwen3.8-27B-ZB4.65-PRO-IQ4_XS**
 ---
 ## Credits & Acknowledgements

 **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:
 ### Comprehensive Comparison Table
+**Updated ranked list** (re-sorted primarily by Mean KLD ascending — lower is better; ties broken by other quality metrics):
 | Rank | Model / File Name | Level / Source | Quant Type | Size (GB) | Mean PPL | Δ PPL | Mean KLD | Same Top-p (%) | KLD 99% |
 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
 | **1** | `Qwen3.8-27B-UD-Q8_K_XL` | unsloth UD2 | UD-Q8_K_XL | 29.30 | 6.953800 | +0.003500 | 0.000850 | 98.970% | — |
 | **4** | `Qwen3.8-27B-Q5_K_M` | unsloth UD2 | Q5_K_M | 18.47 | 6.974200 | +0.023900 | 0.006220 | 96.700% | — |
 | 5 | `Qwen3.8-27B-UD-Q4_K_XL` | unsloth UD2 | Q4_K_XL | 16.69 | 6.979220 | +0.028728 | 0.008606 | 96.091% | 0.091099 |
 | **6** | `Qwen3.8-27B-ZB4.97-GOD-IQ4_XS` | **ZB-GOD** | IQ4_XS | **15.82** | **7.004243** | **+0.053751** | **0.012249** | **95.337%** | **0.117617** |
+| **7** | `Qwen3.8-27B-UD3-Q4_K_S` | **unsloth UD3** | Q4_K_S | **14.30** | **6.969514** | **+0.019022** | **0.013652** | **95.149%** | **0.141744** |
+| **8** | `Qwen3.8-27B-Autoround-Q4_K_M` | intel | Q4_K_M | **15.66** | **6.950294** | **-0.000199** | **0.014657** | **94.859%** | **0.147949** |
+| **9** | `Qwen3.8-27B-ZB4.65-PRO-IQ4_XS` | **ZB-PRO** | IQ4_XS | **14.81** | **7.017278** | **+0.066786** | **0.015466** | **94.766%** | **0.150252** |
+| **10** | `Qwen3.8-27B-Q4_K_M` | unsloth UD2 | Q4_K_M | 15.93 | 6.956100 | +0.005800 | 0.015490 | 94.650% | — |
+| **11** | `Qwen3.8-27B-ZB4.60-PRO-IQ4_XS` | **ZB-PRO** | IQ4_XS | **14.65** | **7.030895** | **+0.080402** | **0.016162** | **94.668%** | **0.159115** |
+| **12** | `Qwen3.8-27B-ZB4.55-PRO-IQ4_XS` | **ZB-PRO** | IQ4_XS | **14.49** | **7.032263** | **+0.081771** | **0.016647** | **94.613%** | **0.161016** |
+| 13 | `Qwen3.8-27B-IQ4_NL` | bartowski | IQ4_NL | 15.20 | 7.006472 | +0.055980 | 0.018427 | 94.230% | 0.190168 |
+| 14 | `Qwen3.8-27B-IQ4_XS` | unsloth UD2 | IQ4_XS | 14.63 | 7.012695 | +0.062202 | 0.018652 | 94.270% | 0.194338 |
+| **15** | `Qwen3.8-27B-UD3-IQ4_XS` | **unsloth UD3** | IQ4_XS | **13.27** | **7.004732** | **+0.054240** | **0.018772** | **93.975%** | **0.195164** |
+| **16** | `Qwen3.8-27B-ZB4.48-STD-IQ4_XS` | **ZB-STD** | IQ4_XS | **14.26** | **7.050096** | **+0.099604** | **0.018892** | **94.199%** | **0.196005** |
+| 17 | `Qwen3.8-27B-Q4_K_S` | unsloth UD2 | Q4_K_S | 15.01 | 6.966826 | +0.016334 | 0.018921 | 94.235% | 0.192749 |
+| 18 | `Qwen3.8-27B-IQ4_XS-i1` | mradermacher | IQ4_XS | 14.26 | 7.012810 | +0.062318 | 0.019271 | 94.141% | 0.197891 |
+| 19 | `Qwen3.8-27B-Q4_K_S-i1` | mradermacher | Q4_K_S | 14.74 | 6.989551 | +0.039059 | 0.019805 | 93.996% | 0.204143 |
+| **20** | `Qwen3.8-27B-ZB4.36-STD-IQ4_XS` | **ZB-STD** | IQ4_XS | **13.88** | **7.054811** | **+0.104319** | **0.020556** | **93.951%** | **0.206689** |
+| 21 | `Qwen3.8-27B-Q4_0-AutoRound-Code` | webhie | Q4_0 | 14.64 | 7.067142 | +0.116650 | 0.026586 | 92.970% | 0.271619 |
+| **22** | `Qwen3.8-27B-ZB4.14-MIN-IQ4_XS` | **ZB-MIN** | IQ4_XS | **13.19** | **7.045689** | **+0.095196** | **0.029334** | **92.799%** | **0.294996** |
+| 23 | `Qwen3.8-27B-IQ4_XS-Smaller_3.96` | jrell | IQ4_XS | 12.61 | 7.252766 | +0.302274 | 0.055499 | 90.090% | 0.551972 |
+**Notes on the new entries :**
+- **UD3-Q4_K_S** (rank 7): Excellent KLD and Same Top-p for its size; strong contender among ~14 GB models.
+- **Autoround-Q4_K_M** (rank 8): Very close to base PPL (slightly better ΔPPL), solid KLD.
+- **UD3-IQ4_XS** (rank 15): Competitive with other IQ4_XS variants, good size/quality trade-off.
+**Update: Aug 20, 2026**
+>The newly released Unsloth Dynamic v3 is truly the best value for performance right now.
+>My ZB is just an experiment, feel free to check it out for fun :)
 ## 3. ZB-ZipBrain Tiers & Recommendations
 * **ZB-STD :** Standard. Similar to other standard IQ4_XS models currently available.
 * **ZB-MIN :** Minimal. Optimal footprint for tight 16 GB memory setups, allowing headroom for longer context windows
 ---
 ## Credits & Acknowledgements