Instructions to use OEvortex/HelpingAI-180B-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OEvortex/HelpingAI-180B-base with Transformers:

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

pipe = pipeline("text-generation", model="OEvortex/HelpingAI-180B-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OEvortex/HelpingAI-180B-base")
model = AutoModelForCausalLM.from_pretrained("OEvortex/HelpingAI-180B-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OEvortex/HelpingAI-180B-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OEvortex/HelpingAI-180B-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OEvortex/HelpingAI-180B-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OEvortex/HelpingAI-180B-base

SGLang

How to use OEvortex/HelpingAI-180B-base 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 "OEvortex/HelpingAI-180B-base" \
    --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": "OEvortex/HelpingAI-180B-base",
		"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 "OEvortex/HelpingAI-180B-base" \
        --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": "OEvortex/HelpingAI-180B-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OEvortex/HelpingAI-180B-base with Docker Model Runner:
```
docker model run hf.co/OEvortex/HelpingAI-180B-base
```

HelpingAI-180B-base

File size: 1,043 Bytes

---
license: mit
language:
- en
pipeline_tag: text-generation
tags:
- gemma
---

# HelpingAI-180B-base

## Description
The HelpingAI-180B-base model is a large-scale artificial intelligence model developed to assist in various natural language processing tasks. Trained on a diverse range of data sources, this model is designed to generate text, facilitate language understanding, and support various downstream tasks.

## Model Information
- **Model size**: 176 billion parameters
- **Training data**: Diverse datasets covering a wide range of topics and domains.
- **Training objective**: Language modeling with an emphasis on understanding and generating human-like text.
- **Tokenizer**: Gemma tokenizer
## Intended Use
The HelpingAI-180B-base model is intended for researchers, developers, and practitioners in the field of natural language processing (NLP). It can be used for a variety of tasks, including but not limited to:
- Text generation
- Language understanding
- Text summarization
- Dialogue generation
This model for research