RichardErkhov/CreitinGameplays_-_bloom-3b-conversational-8bits

Quantization made by Richard Erkhov.

bloom-3b-conversational - bnb 8bits

Model creator: https://huggingface.co/CreitinGameplays/
Original model: https://huggingface.co/CreitinGameplays/bloom-3b-conversational/

Original model description:

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. <|prompter|> who was Nikola Tesla? <|assistant|> - text: >- <|system|> You are a helpful AI assistant. <|prompter|> write a story about a cat. <|assistant|> - text: >- <|system|> You are a helpful AI assistant. <|prompter|> what is an essay? <|assistant|> - text: >- <|system|> You are a helpful AI assistant. <|prompter|> Tell me 5 Brazilian waterfalls to visit. <|assistant|> - text: >- <|system|> You are a helpful AI assistant. <|prompter|> write a story about how a virus called COVID-19 destroyed the world <|assistant|> - text: >- <|system|> You are a helpful AI assistant. <|prompter|> write a short Python program that asks the user for their name and then greets them by name. <|assistant|> - text: >- <|system|> You are a helpful AI assistant. <|prompter|> What can you do? <|assistant|>
inference: parameters: temperature: 0.1 do_sample: true top_k: 50 top_p: 0.10 max_new_tokens: 250 repetition_penalty: 1.155

🌸 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.