Instructions to use Zicara/OPT-30B-Erebus-4bit-128g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zicara/OPT-30B-Erebus-4bit-128g with Transformers:

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

pipe = pipeline("text-generation", model="Zicara/OPT-30B-Erebus-4bit-128g")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Zicara/OPT-30B-Erebus-4bit-128g")
model = AutoModelForCausalLM.from_pretrained("Zicara/OPT-30B-Erebus-4bit-128g", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Zicara/OPT-30B-Erebus-4bit-128g with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zicara/OPT-30B-Erebus-4bit-128g"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zicara/OPT-30B-Erebus-4bit-128g",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Zicara/OPT-30B-Erebus-4bit-128g

SGLang

How to use Zicara/OPT-30B-Erebus-4bit-128g 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 "Zicara/OPT-30B-Erebus-4bit-128g" \
    --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": "Zicara/OPT-30B-Erebus-4bit-128g",
		"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 "Zicara/OPT-30B-Erebus-4bit-128g" \
        --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": "Zicara/OPT-30B-Erebus-4bit-128g",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Zicara/OPT-30B-Erebus-4bit-128g with Docker Model Runner:
```
docker model run hf.co/Zicara/OPT-30B-Erebus-4bit-128g
```

OPT-30B-Erebus-4bit-128g

Model description

Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.

This is a 4-bit GPTQ quantization of OPT-30B-Erebus, original model: https://huggingface.co/KoboldAI/OPT-30B-Erebus

Quantization Information

Quantized with: https://github.com/0cc4m/GPTQ-for-LLaMa

python opt.py --wbits 4 models/OPT-30B-Erebus c4 --groupsize 128 --save models/OPT-30B-Erebus-4bit-128g/OPT-30B-Erebus-4bit-128g.pt
python opt.py --wbits 4 models/OPT-30B-Erebus c4 --groupsize 128 --save_safetensors models/OPT-30B-Erebus-4bit-128g/OPT-30B-Erebus-4bit-128g.safetensors

Output generated in 54.23 seconds (0.87 tokens/s, 47 tokens, context 44, seed 593020441)

Command text-generation-webui:

https://github.com/oobabooga/text-generation-webui

call python server.py --model_type gptj --model OPT-30B-Erebus-4bit-128g --chat --wbits 4 --groupsize 128 --xformers --sdp-attention

Zicara
/

OPT-30B-Erebus-4bit-128g

OPT-30B-Erebus-4bit-128g

Model description

Quantization Information

Command text-generation-webui:

Credit

License

Dataset used to train Zicara/OPT-30B-Erebus-4bit-128g