Instructions to use DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-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 DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf: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 DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf: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 DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf:Q4_K_M

Use Docker

docker model run hf.co/DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-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": "DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf:Q4_K_M

SGLang

How to use DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf with Ollama:
```
ollama run hf.co/DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf:Q4_K_M
```

Unsloth Studio

How to use DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-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 DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-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 DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf to start chatting

How to use DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf: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": "DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-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 DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf: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 DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf: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 "DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf: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"

Docker Model Runner
How to use DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf with Docker Model Runner:
```
docker model run hf.co/DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf:Q4_K_M
```

Lemonade

How to use DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DavidAU/Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf-Q4_K_M

List all available models

lemonade list

DavidAU commited on Jul 28, 2025

Commit

dfc5cef

1 Parent(s): 2c9b46e

Update README.md

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@@ -319,4 +319,44 @@ Because that's what we did. That's what we all did. We reached for each other, e
 I exhaled sharply, the sound feeling like a prayer. "Alright," I murmured, my voice steady now. "Let's not make her a liar, huh?"
-And with that, I pressed the final key.

 I exhaled sharply, the sound feeling like a prayer. "Alright," I murmured, my voice steady now. "Let's not make her a liar, huh?"
+And with that, I pressed the final key.
+---
+<h2>Special Thanks:</h2>
+---
+Special thanks to all the following, and many more...
+All the model makers, fine tuners, mergers, and tweakers:
+- Provides the raw "DNA" for almost all my models.
+- Sources of model(s) can be found on the repo pages, especially the "source" repos with link(s) to the model creator(s).
+Huggingface [ https://huggingface.co ] :
+- The place to store, merge, and tune models endlessly.
+- THE reason we have an open source community.
+LlamaCPP [ https://github.com/ggml-org/llama.cpp ] :
+- The ability to compress and run models on GPU(s), CPU(s) and almost all devices.
+- Imatrix, Quantization, and other tools to tune the quants and the models.
+- Llama-Server : A cli based direct interface to run GGUF models.
+- The only tool I use to quant models.
+Quant-Masters: Team Mradermacher, Bartowski, and many others:
+- Quant models day and night for us all to use.
+- They are the lifeblood of open source access.
+MergeKit [ https://github.com/arcee-ai/mergekit ] :
+- The universal online/offline tool to merge models together and forge something new.
+- Over 20 methods to almost instantly merge model, pull them apart and put them together again.
+- The tool I have used to create over 1500 models.
+Lmstudio [ https://lmstudio.ai/ ] :
+- The go to tool to test and run models in GGUF format.
+- The Tool I use to test/refine and evaluate new models.
+- LMStudio forum on discord; endless info and community for open source.
+Text Generation Webui // KolboldCPP // SillyTavern:
+- Excellent tools to run GGUF models with - [  https://github.com/oobabooga/text-generation-webui ] [ https://github.com/LostRuins/koboldcpp ] .
+- Sillytavern [ https://github.com/SillyTavern/SillyTavern ] can be used with LMSTudio [ https://lmstudio.ai/ ] , TextGen [ https://github.com/oobabooga/text-generation-webui ], Kolboldcpp [ https://github.com/LostRuins/koboldcpp ], Llama-Server [part of LLAMAcpp] as a off the scale front end control system and interface to work with models.