Instructions to use eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained with Transformers:

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

pipe = pipeline("text-generation", model="eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained")
model = AutoModelForCausalLM.from_pretrained("eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained

SGLang

How to use eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained 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 "eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained" \
    --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": "eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained",
		"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 "eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained" \
        --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": "eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained with Docker Model Runner:
```
docker model run hf.co/eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained
```

Model Card for Model ID

This model is pretrained with Vietnamese language, based on GPT-NeoX which is a large language model developed by EleutherAI.

Model Details

Training Data

Pre-train: Culturax Vietnamese Dataset(450GB) + AI-Hub Vietnamese Dataset(1.3GB) + Crawled Vietnamese Wikipedia Dataset(630MB) + viwik18 Dataset(1.27GB)

Training Hardware

Trained on A100 40GB GPU and 48 core CPU. Took about 17 hours to reach 80,000 steps.

Hyperparameters

Hyperparameter	Value
n_parameters	2670182400
n_layers	32
d_model	2560
n_heads	32
d_head	128
n_vocab	60000
Sequence Length	2048
Learning Rate	0.00016
Positional Encoding	Rotary Position Embedding (RoPE)

How to use

The model can be loaded using the AutoModelForCausalLM functionality:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained")
model = AutoModelForCausalLM.from_pretrained("eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained")

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Paper for eunyounglee/GPT-NeoX-2.7B-Vietnamese-pretrained

RoFormer: Enhanced Transformer with Rotary Position Embedding

Paper • 2104.09864 • Published Apr 20, 2021 • 18