Instructions to use ArtusDev/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-EXL3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArtusDev/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-EXL3 with Transformers:

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

pipe = pipeline("text-generation", model="ArtusDev/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-EXL3")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ArtusDev/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-EXL3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArtusDev/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-EXL3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArtusDev/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-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/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-EXL3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ArtusDev/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-EXL3

SGLang

How to use ArtusDev/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-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/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-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/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-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/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-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/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-EXL3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ArtusDev/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-EXL3 with Docker Model Runner:
```
docker model run hf.co/ArtusDev/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-EXL3
```

EXL3 Quants of Doctor-Shotgun/L3.3-70B-Magnum-Diamond

EXL3 quants of Doctor-Shotgun/L3.3-70B-Magnum-Diamond using exllamav3 for quantization.

Quants

Quant(Revision)	Bits per Weight	Head Bits
2.5_H6	2.5	6
3.0_H6	3.0	6
3.5_H6	3.5	6
4.0_H6	4.0	6
4.25_H6	4.25	6
4.5_H6	4.5	6
5.0_H6	5.0	6
6.0_H6	6.0	6
8.0_H6	8.0	6
8.0_H8	8.0	8

Downloading quants with huggingface-cli

Click to view download instructions

Install hugginface-cli:

pip install -U "huggingface_hub[cli]"

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

huggingface-cli download ArtusDev/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-EXL3 --revision "5.0bpw_H6" --local-dir ./

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for ArtusDev/Doctor-Shotgun_L3.3-70B-Magnum-Diamond-EXL3

Base model

meta-llama/Llama-3.1-70B

Finetuned

meta-llama/Llama-3.3-70B-Instruct

Finetuned

Doctor-Shotgun/L3.3-70B-Magnum-Diamond

Quantized

(4)

this model