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

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
```

Model size for int4 fine tuning on rtx 3090

by KnutJaegersberg - opened Apr 15, 2023

Discussion

KnutJaegersberg

Apr 15, 2023

I don't know when HF releases support for int4 fine tuning. Others are alread onto building it.
Since llama 30b is properly the best model that fits on an rtx 3090, I guess, this model here could be used as well. However, the original weights quantized to int4 for fine tuning will be useful, too.
I think lora fine tuning does not depend a lot on parameter count. It is possible to lora fine tune gptneox 20b in 8 bit.
I'd guess it should be possible to lora fine tune llama 30b int4 on an rtx 3090.
Will you watch this space, too?
Such a base model would be very valuable to the community, I'd guess.

MetaIX

Owner Apr 23, 2023

There are some people fine-tuning on 4-bit already. See: https://github.com/johnsmith0031/alpaca_lora_4bit

KnutJaegersberg

Apr 23, 2023

learned about that only yesterday. I heard hf library got an update and it might be necessary to reconvert the weights. how does that all the happy fine tuning? will the models still work in the future?

Acrious

May 4, 2023

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