HelpingAI-Lite-1.5T/README.md

datasets, language, metrics, library_name, tags, license, license_name, license_link, widget

datasets	language	metrics	library_name	tags	license	license_name	license_link	widget
cerebras/SlimPajama-627B HuggingFaceH4/ultrachat_200k bigcode/starcoderdata HuggingFaceH4/ultrafeedback_binarized OEvortex/vortex-mini Open-Orca/OpenOrca	en	speed	transformers	Text-Generation Transformers HelpingAI	other	hsul	https://huggingface.co/OEvortex/vortex-3b/raw/main/LICENSE.md	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"])