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! <|user|> Write me a function to search for OEvortex on youtube use Webbrowser . <|assistant|>
• text: | <|system|> You are a chatbot who can be a teacher! <|user|> Explain me working of AI . <|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

QuantFactory/HelpingAI-Lite-GGUF

This is quantized version of OEvortex/HelpingAI-Lite created using llama.cpp

Original Model Card

HelpingAI-Lite

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GGUF version here

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

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"])