README.md · CreitinGameplays/bloom-3b-conversational at cda2e7a9ecb543939a01eb0169b1c1c0db384f40

metadata

license: mit
datasets:
  - Xilabs/instructmix
  - CreitinGameplays/small-chat-assistant-for-bloom
  - sahil2801/CodeAlpaca-20k
language:
  - en
tags:
  - uncensored
  - unrestricted
  - code
  - biology
  - chemistry
  - finance
  - legal
  - music
  - art
  - climate
  - merge
  - text-generation-inference
  - moe
widget:
  - text: >-
      <|system|> You are a helpful AI assistant. </s> <|prompter|> who was
      Nikola Tesla? </s> <|assistant|>
  - text: >-
      <|system|> You are a helpful AI assistant. </s> <|prompter|> write a story
      about a cat. </s> <|assistant|>
  - text: >-
      <|system|> You are a helpful AI assistant. </s> <|prompter|> what is an
      essay? </s> <|assistant|>
  - text: >-
      <|system|> You are a helpful AI assistant. </s> <|prompter|> Tell me 5
      Brazilian waterfalls to visit. </s> <|assistant|>
  - text: >-
      <|system|> You are a helpful AI assistant. </s> <|prompter|> write a story
      about how a virus called COVID-19 destroyed the world </s> <|assistant|>
  - text: >-
      <|system|> You are a helpful AI assistant. </s> <|prompter|> write a short
      Python program that asks the user for their name and then greets them by
      name. </s> <|assistant|>
inference:
  parameters:
    temperature: 0.2
    do_sample: true
    top_p: 0.95
    max_new_tokens: 250
    repetition_penalty: 1.165

BLOOM 3b Fine-tuned for Chat Assistant

Run this model on Kaggle Notebook

Model Name: bloom-3b-conversational

Model Architecture: bloom

Short Description: This model is a fine-tuned version of the BLOOM 3b language model, focusing on conversational interactions between an user and an AI assistant.

Intended Use: This model is intended for research purposes and exploration of conversational AI applications. It can be used for tasks like:

Generating responses to user prompts in a chat assistant setting.
Creating examples of chatbot interactions for further development.
Studying the capabilities of language models for conversation.

Limitations:

Fine-tuning Focus: The model's performance is optimized for the specific format and context of the fine-tuning data. It may not generalize well to significantly different conversation styles or topics.
Potential Biases: The model may inherit biases from the training data. It's important to be aware of these potential biases and use the model responsibly.
Limited Factual Accuracy: Language models are still under development and may generate responses that are not entirely factually accurate. It's important to verify information generated by the model with other sources.
Primarily English: While the model can respond in other languages, the quality and accuracy of its responses may be lower compared to English. This is because the model was primarily fine-tuned on English data.

Specific Input Format:

The model was fine-tuned using a specific input format that goes like this:

<|system|> {system prompt} </s>  <|prompter|> {user prompt} </s> <|assistant|> {model response} ```

Using this format when interacting with the model can improve its performance and generate more relevant responses.

**Disclaimer:** This model is for research and exploration purposes only. It should not be used in any applications that require high levels of accuracy or reliability.