README.md

---
datasets:
- cerebras/SlimPajama-627B
- HuggingFaceH4/ultrachat_200k
- bigcode/starcoderdata
- HuggingFaceH4/ultrafeedback_binarized
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|>
model-index:
- name: HelpingAI-Lite
  results:
  - task:
      type: text-generation
    metrics:
    - name: Epoch
      type: Training Epoch
      value: 3
    - name: Eval Logits/Chosen
      type: Evaluation Logits for Chosen Samples
      value: -2.707406759262085
    - name: Eval Logits/Rejected
      type: Evaluation Logits for Rejected Samples
      value: -2.65652441978546
    - name: Eval Logps/Chosen
      type: Evaluation Log-probabilities for Chosen Samples
      value: -370.129670421875
    - name: Eval Logps/Rejected
      type: Evaluation Log-probabilities for Rejected Samples
      value: -296.073825390625
    - name: Eval Loss
      type: Evaluation Loss
      value: 0.513750433921814
    - name: Eval Rewards/Accuracies
      type: Evaluation Rewards and Accuracies
      value: 0.738095223903656
    - name: Eval Rewards/Chosen
      type: Evaluation Rewards for Chosen Samples
      value: -0.0274422804903984
    - name: Eval Rewards/Margins
      type: Evaluation Rewards Margins
      value: 1.008722543614307
    - name: Eval Rewards/Rejected
      type: Evaluation Rewards for Rejected Samples
      value: -1.03616464138031
    - name: Eval Runtime
      type: Evaluation Runtime
      value: 93.5908
    - name: Eval Samples
      type: Number of Evaluation Samples
      value: 2000
    - name: Eval Samples per Second
      type: Evaluation Samples per Second
      value: 21.37
    - name: Eval Steps per Second
      type: Evaluation Steps per Second
      value: 0.673
---

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

GGUF version [here](https://huggingface.co/OEvortex/HelpingAI-Lite-GGUF)

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

## 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"])
```