README.md

metadata

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

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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

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"])