Instructions to use staccs/Qwen3-32B-lecore-19g-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 staccs/Qwen3-32B-lecore-19g-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 staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf staccs/Qwen3-32B-lecore-19g-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 staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf staccs/Qwen3-32B-lecore-19g-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 staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf staccs/Qwen3-32B-lecore-19g-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 staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M

Use Docker

docker model run hf.co/staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use staccs/Qwen3-32B-lecore-19g-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "staccs/Qwen3-32B-lecore-19g-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": "staccs/Qwen3-32B-lecore-19g-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M

Ollama
How to use staccs/Qwen3-32B-lecore-19g-GGUF with Ollama:
```
ollama run hf.co/staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M
```
Unsloth Desktop

How to use staccs/Qwen3-32B-lecore-19g-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf staccs/Qwen3-32B-lecore-19g-GGUF: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": "staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use staccs/Qwen3-32B-lecore-19g-GGUF with Docker Model Runner:
```
docker model run hf.co/staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M
```

Lemonade

How to use staccs/Qwen3-32B-lecore-19g-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-32B-lecore-19g-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use staccs/Qwen3-32B-lecore-19g-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 staccs/Qwen3-32B-lecore-19g-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 staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use staccs/Qwen3-32B-lecore-19g-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf staccs/Qwen3-32B-lecore-19g-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 "staccs/Qwen3-32B-lecore-19g-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"

Qwen3-32B-lecore-19g-GGUF

GGUF builds of Qwen/Qwen3-32B for llama.cpp, Ollama and LM Studio. These are companions to the MLX export staccs/Qwen3-32B-lecore-19g-mlx, which has the measured perplexity numbers.

File	Size	What it is
`Qwen3-32B-lecore-19g-GGUF-Q4_K_M.gguf`	19.76 GB	stock llama.cpp Q4_K_M
`Qwen3-32B-lecore-19g-GGUF-lecore-mix.gguf`	18.64 GB	leCore per-tensor mix: each linear layer gets the GGUF K-quant matching the bits leCore measured for it (2→Q2_K, 3→Q3_K, 4→Q4_K, 8→Q8_0); embeddings and output follow the MLX export

The lecore-mix file is quantized from the original bf16 weights with llama.cpp K-quants. The bit width of each tensor follows leCore's measured (KL-damage) allocation, so its numbers are close to, but not identical to, the MLX export. Some tensor widths are not multiples of 256, so llama.cpp may fall back to a compatible type for those tensors.

Note: at this budget every linear tensor was allocated 4 bits, so lecore-mix is in practice a uniform Q4_K build (with a Q6_K output layer). GSM8K regressed in the MLX export. See the MLX card for the diagnosis.

Usage

Ollama: download a file, then

printf 'FROM ./Qwen3-32B-lecore-19g-GGUF-Q4_K_M.gguf
' > Modelfile && ollama create qwen3-32b-lecore-19g-gguf -f Modelfile && ollama run qwen3-32b-lecore-19g-gguf

or pull straight from the Hub with ollama run hf.co/staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M

LM Studio: search for staccs/Qwen3-32B-lecore-19g-GGUF in the model browser and pick a file. On a 36 GB Mac, the lecore-mix file is the one that fits.

llama.cpp

llama-cli -hf staccs/Qwen3-32B-lecore-19g-GGUF -p "Hello" -n 256

Benchmarks (lm-eval-harness 0.4.9, run on the shipped weights dequantized to bf16 on GPU)

MMLU 5-shot on the first 10 questions of each of the 57 subjects; GSM8K 5-shot on the first 100 test problems (greedy, 320 new tokens, no chat template). These are subsets, so expect about ±2–4 points of sampling noise.

Model	MMLU (5-shot)	GSM8K strict	GSM8K flexible
leCore MLX export (numbers measured on the MLX weights; these GGUF files were not benchmarked)	85.3%	27.0%	34.0%
stock 3-bit affine, group 64 (simulated)	80.9%	83.0%	78.0%

License

Base model: Qwen/Qwen3-32B, released by the Qwen team under the Apache License 2.0 (https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE). Only quantization was done here: no retraining.

Downloads last month: 315

GGUF

Model size

33B params

Architecture

qwen3

Hardware compatibility

4-bit

View +1 variant

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