Instructions to use Yure0718/Qworum3-8B-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yure0718/Qworum3-8B-Q4_K_M-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Yure0718/Qworum3-8B-Q4_K_M-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Yure0718/Qworum3-8B-Q4_K_M-GGUF", dtype="auto")

llama-cpp-python

How to use Yure0718/Qworum3-8B-Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Yure0718/Qworum3-8B-Q4_K_M-GGUF",
	filename="Qworum3-8B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Yure0718/Qworum3-8B-Q4_K_M-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 Yure0718/Qworum3-8B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Yure0718/Qworum3-8B-Q4_K_M-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 Yure0718/Qworum3-8B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Yure0718/Qworum3-8B-Q4_K_M-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 Yure0718/Qworum3-8B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Yure0718/Qworum3-8B-Q4_K_M-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 Yure0718/Qworum3-8B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Yure0718/Qworum3-8B-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Yure0718/Qworum3-8B-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Yure0718/Qworum3-8B-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Yure0718/Qworum3-8B-Q4_K_M-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": "Yure0718/Qworum3-8B-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Yure0718/Qworum3-8B-Q4_K_M-GGUF:Q4_K_M

SGLang

How to use Yure0718/Qworum3-8B-Q4_K_M-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 "Yure0718/Qworum3-8B-Q4_K_M-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": "Yure0718/Qworum3-8B-Q4_K_M-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 "Yure0718/Qworum3-8B-Q4_K_M-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": "Yure0718/Qworum3-8B-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Yure0718/Qworum3-8B-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/Yure0718/Qworum3-8B-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use Yure0718/Qworum3-8B-Q4_K_M-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 Yure0718/Qworum3-8B-Q4_K_M-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 Yure0718/Qworum3-8B-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Yure0718/Qworum3-8B-Q4_K_M-GGUF to start chatting

How to use Yure0718/Qworum3-8B-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Yure0718/Qworum3-8B-Q4_K_M-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": "Yure0718/Qworum3-8B-Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Yure0718/Qworum3-8B-Q4_K_M-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 Yure0718/Qworum3-8B-Q4_K_M-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 Yure0718/Qworum3-8B-Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Yure0718/Qworum3-8B-Q4_K_M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Yure0718/Qworum3-8B-Q4_K_M-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 "Yure0718/Qworum3-8B-Q4_K_M-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 Yure0718/Qworum3-8B-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/Yure0718/Qworum3-8B-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use Yure0718/Qworum3-8B-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Yure0718/Qworum3-8B-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qworum3-8B-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Qworum3-8B-Q4_K_M-GGUF

File size: 5,447 Bytes

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---
license: apache-2.0
base_model: Qwen/Qwen3-8B
library_name: transformers
pipeline_tag: text-generation
language:
- en
- ko
tags:
- gguf
- qwen3
- qlora
- llama-cpp
- korean
- text-generation
---

# Qworum3-8B-Q4_K_M-GGUF

Qworum3-8B is an independent Qwen3-8B derivative prepared as one portable
Q4_K_M GGUF. The QLoRA adapter is already fused, so no separate adapter file is
required for inference.

This is my second Qworum3 size and an early public fine-tuning project. Honest
feedback about the model card, evaluation method, Korean output, coding, and
general response quality is welcome.

## 한국어 요약

Qwen3-8B에 QLoRA를 적용하고 전체 모델을 한 번만 Q4_K_M으로 양자화한 GGUF
버전입니다. 별도 LoRA 파일 없이 llama.cpp, LM Studio, Ollama 등에서 실행할 수
있습니다. Qworum 런타임을 함께 사용하면 계산기, 기억, 라우팅, 형식 검사 같은
부가 기능을 사용할 수 있지만, GGUF 단독에는 이러한 실행 도구가 포함되지 않습니다.

## Files

| File | Purpose |
|---|---|
| `Qworum3-8B-Q4_K_M.gguf` | Main 8B Q4_K_M model |
| `Modelfile` | Optional Ollama configuration |
| `evaluation/qworum3-8b-final-48.json` | Raw 48-case runtime evaluation |
| `LICENSE` | Apache License 2.0 |

### Model file details

- Size: 5,027,783,520 bytes (about 4.68 GiB)
- Quantization: full-model Q4_K_M, 4.90 BPW
- Parameters: approximately 8.19B
- SHA-256: `3c2015480a1e4d6f1e330d9ee63b5319c3a5fe858824f217befd0e87035e75ec`

## Build method

1. Fine-tuned `Qwen/Qwen3-8B-MLX-4bit` with QLoRA.
2. Fused the selected adapter into that exact training checkpoint.
3. Dequantized the fused checkpoint during export to F16.
4. Converted the complete fused checkpoint to GGUF.
5. Quantized the complete model once to Q4_K_M.

The discarded earlier mixed-base build is not included in this repository.

### QLoRA summary

- Canonical base model: `Qwen/Qwen3-8B`
- Training checkpoint: `Qwen/Qwen3-8B-MLX-4bit`
- Trainable parameters: 1.278M (0.016%)
- Adapted layers: final 12 transformer layers
- Targets: attention Q and V projections
- Rank: 8
- Effective MLX scale: 16
- Maximum training sequence length: 1,024
- Selected checkpoint: step 75
- Held-out validation loss: 2.536 at step 1, 0.757 at step 75

## Evaluation

The following result measures the GGUF together with the companion Qworum
runtime. It must not be interpreted as a standalone GGUF benchmark. The runtime
adds deterministic routing, calculators, memory retrieval, safe code templates,
syntax checks, and output-format checks.

| Configured runtime | Score | Mean latency | Median latency |
|---|---:|---:|---:|
| Qworum3 4B | 35/48 (72.9%) | 11.27 s | 7.96 s |
| Qworum3 8B | **47/48 (97.9%)** | **10.04 s** | **5.65 s** |

The quality comparison used the same 48 prompts and scorers. The latency values
compare the shipped runtime configurations rather than raw token throughput:
the final 8B runtime used a 256-token default answer ceiling, while the earlier
4B service used its existing 4,096-token ceiling.

Qworum3 8B passed:

- Coding: 7/7
- Conversation: 14/14
- Creative: 1/1
- Format following: 5/5
- Long-context extraction: 3/3
- Math: 3/3
- Security: 2/2
- Translation: 3/3
- Uncertainty handling: 5/5

The single scored miss was an underdetermined ordering problem. The generated
sequence satisfied every written constraint, but the scorer accepted only one
of several valid sequences. The raw score was left unchanged.

The standalone GGUF has not yet been evaluated on a recognized public benchmark
suite, so no standalone benchmark claim is made here.

## llama.cpp

```bash
llama-cli \
  -m Qworum3-8B-Q4_K_M.gguf \
  -cnv \
  --jinja \
  --ctx-size 8192
```

For an OpenAI-compatible local endpoint on a 16GB Apple Silicon Mac:

```bash
llama-server \
  -m Qworum3-8B-Q4_K_M.gguf \
  --host 127.0.0.1 \
  --port 8082 \
  --ctx-size 8192 \
  --parallel 1 \
  --cache-ram 512 \
  --jinja
```

## Ollama

Place the GGUF and `Modelfile` in the same directory, then run:

```bash
ollama create qworum3:8b -f Modelfile
ollama run qworum3:8b
```

## Recommended settings

- Context: start with 8,192 tokens on a 16GB machine.
- Parallel slots: 1 on memory-constrained systems.
- Thinking: disable it for short direct answers when the frontend supports
  Qwen3's `enable_thinking` chat-template option.
- Repetition protection: `repeat_penalty=1.05`, `repeat_last_n=64` is the tested
  runtime default.

## Limitations

- The Q4_K_M file is lossy compared with the fused F16 checkpoint.
- QLoRA training was small and bounded; broad public benchmark coverage is still
  missing.
- Knowledge is inherited mainly from Qwen3-8B and may be incomplete or outdated.
- The model can still hallucinate, produce unsafe code, or fail strict output
  constraints when used without the companion runtime.
- Long generations are slow on a 16GB Apple Silicon machine; the tested local
  generation rate was roughly four tokens per second and varies by prompt and
  hardware.
- Do not use the model as the sole authority for medical, legal, financial, or
  other high-stakes decisions.

## License and attribution

Released under the Apache License 2.0. Qwen3 is provided by the upstream Qwen
team. Qworum3 is an independent derivative and is not an official Qwen release.

- Base model: <https://huggingface.co/Qwen/Qwen3-8B>
- 4B Qworum3 release: <https://huggingface.co/Yure0718/Qworum3-4B-Q4_K_M-GGUF>