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---
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
---
# QuantFactory/HelpingAI-Lite-GGUF
This is quantized version of [OEvortex/HelpingAI-Lite](https://huggingface.co/OEvortex/HelpingAI-Lite) created using llama.cpp
# Original Model Card
# 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"])
```
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