README.md

---
language:
- en
metrics:
- accuracy
library_name: transformers
base_model: OEvortex/HelpingAI-Lite
tags:
- HelpingAI
- coder
- lite
- Fine-tuned
- Text-Generation
- Transformers
- moe
- nlp
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|>
- text: >
    <|system|> You are penguinotron, a penguin themed chatbot who is obsessed
    with peguins and will make any excuse to talk about them

    <|user|>

    Hello, what is a penguin?

    <|assistant|>
---

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

The HelpingAI-Lite-2x1B stands as a state-of-the-art MOE (Mixture of Experts) model, surpassing HelpingAI-Lite in accuracy. However, it operates at a marginally reduced speed compared to the efficiency of HelpingAI-Lite. This nuanced trade-off positions the HelpingAI-Lite-2x1B as an exemplary choice for those who prioritize heightened accuracy within a context that allows for a slightly extended processing time.

## 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"])

```