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Check out the documentation for more information.
Quantization made by Richard Erkhov.
HelpingAI-Lite - AWQ
- Model creator: https://huggingface.co/OEvortex/
- Original model: https://huggingface.co/OEvortex/HelpingAI-Lite/
Original model description:
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
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"])
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