README.md

---
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:**
```python
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 interacting with a sophisticated chatbot model optimized for coding tasks!",
    },
    {
        "role": "user",
        "content": "Please generate a Python function that calculates the factorial of a given number.",
    },
]

# 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"])
```