README.md

---
datasets:
- cerebras/SlimPajama-627B
- HuggingFaceH4/ultrachat_200k
- bigcode/starcoderdata
- HuggingFaceH4/ultrafeedback_binarized
- OEvortex/vortex-mini
- Open-Orca/OpenOrca
language:
- en
metrics:
- accuracy
- speed
library_name: transformers
tags:
- coder
- Text-Generation
- Transformers
- HelpingAI
license: mit
widget:
- text: |
    <|system|>
    You are a chatbot who can code!</s>
    <|user|>
    Write me a function to search for OEvortex on youtube use Webbrowser .</s>
    <|assistant|>
- text: |
    <|system|>
    You are a chatbot who can be a teacher!</s>
    <|user|>
    Explain me working of AI .</s>
    <|assistant|>
---

# HelpingAI-Lite-1T
# Subscribe to my YouTube channel
[Subscribe](https://youtube.com/@OEvortex)


HelpingAI-Lite is a lite version of the HelpingAI model that can assist with coding tasks. It's trained on a diverse range of datasets and fine-tuned to provide accurate and helpful responses.

## License

This model is licensed under MIT.

## Datasets

The model was trained on the following datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
- OEvortex/vortex-mini
- Open-Orca/OpenOrca


## Language

The model supports English language.

## Usage

# CPU and GPU code

```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", device=accelerator.device)

# Define the messages
messages = [
    {
        "role": "system",
        "content": "You are a chatbot who can help code!",
    },
    {
        "role": "user",
        "content": "Write me a function to calculate the first 10 digits of the fibonacci sequence in Python and print it out to the CLI.",
    },
]

# 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"])
```