Instructions to use MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2")
model = AutoModelForCausalLM.from_pretrained("MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2

SGLang

How to use MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2 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 "MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2" \
    --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": "MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2",
		"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 "MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2" \
        --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": "MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2
```

Model Card for Model ID

My first exl2 quant of my favourite go-to roleplaying model. Can fit into my empty 24GB VRAM with 32k context in 8-bit cache. Might do a 4.25bpw quant later.

Original model: https://huggingface.co/Doctor-Shotgun/Nous-Capybara-limarpv3-34B

Prompt format: https://github.com/tatsu-lab/stanford_alpaca

Downloads last month: 3

Dataset used to train MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2

Collection including MarinaraSpaghetti/Doctor-Shotgun_Nous-Capybara-limarpv3-34B-4.2bpw-h6-exl2

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