Instructions to use ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3 with Transformers:

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

pipe = pipeline("text-generation", model="ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3

SGLang

How to use ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3 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 "ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3 with Docker Model Runner:
```
docker model run hf.co/ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3
```

soob3123_GrayLine-Qwen3-8B-EXL3

File size: 1,926 Bytes

---
base_model: soob3123/GrayLine-Qwen3-8B
base_model_relation: quantized
quantized_by: ArtusDev
pipeline_tag: text-generation
library_name: transformers
language:
- en
tags:
- text-generation
- uncensored
- direct-answer
- information-retrieval
- general-knowledge
- unfiltered
- amoral-ai
- exl3
datasets:
- soob3123/GrayLine-QA
- soob3123/GrayLine-QA-Reasoning
license: apache-2.0
---

## EXL3 Quants of soob3123/GrayLine-Qwen3-8B

EXL3 quants of [soob3123/GrayLine-Qwen3-8B](https://huggingface.co/soob3123/GrayLine-Qwen3-8B) using <a href="https://github.com/turboderp-org/exllamav3/">exllamav3</a> for quantization.

### Quants
| Quant(Revision) | Bits per Weight | Head Bits |
| -------- | ---------- | --------- |
| [2.5_H6](https://huggingface.co/ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3/tree/2.5bpw_H6) | 2.5 | 6 |
| [3.0_H6](https://huggingface.co/ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3/tree/3.0bpw_H6) | 3.0 | 6 |
| [3.5_H6](https://huggingface.co/ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3/tree/3.5bpw_H6) | 3.5 | 6 |
| [4.0_H6](https://huggingface.co/ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3/tree/4.0bpw_H6) | 4.0 | 6 |
| [4.5_H6](https://huggingface.co/ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3/tree/4.5bpw_H6) | 4.5 | 6 |
| [5.0_H6](https://huggingface.co/ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3/tree/5.0bpw_H6) | 5.0 | 6 |
| [6.0_H6](https://huggingface.co/ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3/tree/6.0bpw_H6) | 6.0 | 6 |
| [8.0_H8](https://huggingface.co/ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3/tree/8.0bpw_H8) | 8.0 | 8 |

### Downloading quants with huggingface-cli

<details>
  <summary>Click to view download instructions</summary>

Install hugginface-cli:

```bash
pip install -U "huggingface_hub[cli]"
```

Download quant by targeting the specific quant revision (branch):

```
huggingface-cli download ArtusDev/soob3123_GrayLine-Qwen3-8B-EXL3 --revision "5.0bpw_H6" --local-dir ./
```
</details>