metadata
datasets:
- cerebras/SlimPajama-627B
- HuggingFaceH4/ultrachat_200k
- bigcode/starcoderdata
- HuggingFaceH4/ultrafeedback_binarized
- OEvortex/vortex-mini
- Open-Orca/OpenOrca
language:
- en
metrics:
- speed
library_name: transformers
tags:
- Text-Generation
- Transformers
- HelpingAI
license: other
license_name: hsul
license_link: https://huggingface.co/OEvortex/vortex-3b/raw/main/LICENSE.md
widget:
- text: |
<|system|>
You are a chatbot who can be a teacher!</s>
<|user|>
Explain me working of AI .</s>
<|assistant|>
🌟 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"])