Instructions to use OEvortex/HelpingAI-Lite-1.5T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OEvortex/HelpingAI-Lite-1.5T with Transformers:

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

pipe = pipeline("text-generation", model="OEvortex/HelpingAI-Lite-1.5T")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OEvortex/HelpingAI-Lite-1.5T")
model = AutoModelForCausalLM.from_pretrained("OEvortex/HelpingAI-Lite-1.5T")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OEvortex/HelpingAI-Lite-1.5T with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OEvortex/HelpingAI-Lite-1.5T"
# 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-Lite-1.5T",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OEvortex/HelpingAI-Lite-1.5T

SGLang

How to use OEvortex/HelpingAI-Lite-1.5T 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-Lite-1.5T" \
    --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-Lite-1.5T",
		"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-Lite-1.5T" \
        --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-Lite-1.5T",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OEvortex/HelpingAI-Lite-1.5T with Docker Model Runner:
```
docker model run hf.co/OEvortex/HelpingAI-Lite-1.5T
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🌟 HelpingAI-Lite-1.5T Model Card 🌟

📊 Datasets used:

cerebras/SlimPajama-627B
HuggingFaceH4/ultrachat_200k
bigcode/starcoderdata
HuggingFaceH4/ultrafeedback_binarized
OEvortex/vortex-mini
Open-Orca/OpenOrca

🗣️ Language:

English (en)

🔒 License:

HelpingAI Simplified Universal License (HSUL)

🧠 Model Overview: HelpingAI-Lite-1.5T is an advanced version of the HelpingAI-Lite model, trained on a vast corpus of 1.5 trillion tokens. This extensive training data enables the model to provide precise and insightful responses, particularly for coding tasks.

🔧 Usage Example:

from transformers import pipeline
from accelerate import Accelerator

# Initialize the accelerator
accelerator = Accelerator()

# Initialize the pipeline
pipe = pipeline("text-generation", model="OEvortex/HelpingAI-Lite-1.5T", device=accelerator.device)

# Define the messages
messages = [
    {
        "role": "system",
        "content": "You are a chatbot who can be a teacher",
    },
    {
        "role": "user",
        "content": "Explain me working of AI.",
    },
]

# Prepare the prompt
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Generate predictions
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)

# Print the generated text
print(outputs[0]["generated_text"])

Downloads last month: 879

Safetensors

Model size

1B params

Tensor type

BF16

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Quantizations

2 models

OEvortex
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HelpingAI-Lite-1.5T

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