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

Libraries

How to use MetaIX/GPT4-X-Alpaca-30B-4bit with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

docker model run hf.co/MetaIX/GPT4-X-Alpaca-30B-4bit

SGLang

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

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

Can someone please help me to understand how can I use this Model card?

#15

by arkaprovob - opened May 27, 2023

Discussion

arkaprovob

May 27, 2023

I would like to download the model card named "gpt4-x-alpaca-30b-ggml-q5_1.bin" to my computer and use Python programming to make queries using this model and then, I want to train the model with my own data. I am new to the field of large language models (LLM) and don't have any previous experience. Can someone please guide me on how, to begin with these requirements?

arkaprovob

May 27, 2023

I can understand that this is probably not the right place, but right now, I am feeling so helpless, trying every possible way to find a solution to my queries.

fblgit

May 27, 2023

•

edited May 27, 2023

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

and by all means, follow the instructions and it will work. You must account hardware requirements.
This model needs a 32G+ on CPU, and about almost 23ish on a GPU.

arkaprovob

May 28, 2023

@fblgit Thank you for your reply. I am aware of oobabooga and have been experimenting with it for the past couple of days. I plan to write a program that uses this model and adds more domain-specific knowledge to it by adding more training data, then query from it (customize it for my own needs) and share it with others if it is successful.

arkaprovob

Jun 25, 2023

Found it.

arkaprovob changed discussion status to closed Jun 25, 2023

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