---
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|>
- text: >-
    <|system|> You are a helpful AI assistant. </s> <|prompter|> What can you do? </s> <|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

![bloom](https://creitingameplays.xyz/img/bloom.png)

**Run this model on [Kaggle Notebook](https://www.kaggle.com/code/creitingameplays/lm-machine-bloom-3b/notebook)**

**Model Name:** bloom-3b-conversational

**Model Architecture:** bloom

**Short Description:** This model is a fine-tuned version of the [BLOOM 3b language model](https://huggingface.co/bigscience/bloom-3b), 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.