Instructions to use MetaIX/GPT4-X-Alpasta-30b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MetaIX/GPT4-X-Alpasta-30b-4bit with Transformers:

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

pipe = pipeline("text-generation", model="MetaIX/GPT4-X-Alpasta-30b-4bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MetaIX/GPT4-X-Alpasta-30b-4bit")
model = AutoModelForCausalLM.from_pretrained("MetaIX/GPT4-X-Alpasta-30b-4bit")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MetaIX/GPT4-X-Alpasta-30b-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MetaIX/GPT4-X-Alpasta-30b-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MetaIX/GPT4-X-Alpasta-30b-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MetaIX/GPT4-X-Alpasta-30b-4bit

SGLang

How to use MetaIX/GPT4-X-Alpasta-30b-4bit 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 "MetaIX/GPT4-X-Alpasta-30b-4bit" \
    --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": "MetaIX/GPT4-X-Alpasta-30b-4bit",
		"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 "MetaIX/GPT4-X-Alpasta-30b-4bit" \
        --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": "MetaIX/GPT4-X-Alpasta-30b-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MetaIX/GPT4-X-Alpasta-30b-4bit with Docker Model Runner:
```
docker model run hf.co/MetaIX/GPT4-X-Alpasta-30b-4bit
```

Any chance of a 4_2 or 4_0 ggml quantization?

by snakerassler - opened May 4, 2023

Discussion

snakerassler

May 4, 2023

I'm running on an m2 pro and 30b with 4_1 is just out of my reach in terms of speed / memory, but those other quant formats work great.

Thanks for all the work you're doing for this community BTW!!

Erilaz

May 5, 2023

q4_0, q5_0, yeah. Both are more efficient than q4_1 and can fit bloated (cought cough Windows) 32GB RAM systems easier.

MetaIX

Owner May 6, 2023

I'm updating the model with the latest version of Open Assistant. I'll update the ggml quants and add q5_0

Morivy

May 7, 2023

Any chance for a q5_1 version?

snakerassler

May 7, 2023

Thanks for the new quants!!

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