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
```

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Check out the documentation for more information.

Information

GPT4-X-Alpaca 30B 4-bit working with GPTQ versions used in Oobabooga's Text Generation Webui and KoboldAI.

This was made using Chansung's GPT4-Alpaca Lora

Update 05.26.2023

Updated the ggml quantizations to be compatible with the latest version of llamacpp (again).

What's included

GPTQ: 2 quantized versions. One quantized --true-sequential and act-order optimizations, and the other was quantized using --true-sequential --groupsize 128 optimizations

GGML: 3 quantized versions. One quantized using q4_1, another one was quantized using q5_0, and the last one was quantized using q5_1.

GPU/GPTQ Usage

To use with your GPU using GPTQ pick one of the .safetensors along with all of the .jsons and .model files.

Oobabooga: If you require further instruction, see here and here

KoboldAI: If you require further instruction, see here

CPU/GGML Usage

To use your CPU using GGML(Llamacpp) you only need the single .bin ggml file.

Oobabooga: If you require further instruction, see here

KoboldAI: If you require further instruction, see here

Training Parameters

num_epochs=10
cutoff_len=512
group_by_length
lora_target_modules='[q_proj,k_proj,v_proj,o_proj]'
lora_r=16
micro_batch_size=8

Benchmarks

--true-sequential --act-order

Wikitext2: 4.481280326843262

Ptb-New: 8.539161682128906

C4-New: 6.451964855194092

Note: This version does not use --groupsize 128, therefore evaluations are minimally higher. However, this version allows fitting the whole model at full context using only 24GB VRAM.

--true-sequential --groupsize 128

Wikitext2: 4.285132884979248

Ptb-New: 8.34856128692627

C4-New: 6.292652130126953

Note: This version uses --groupsize 128, resulting in better evaluations. However, it consumes more VRAM.

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