README.md

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license: apache-2.0
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  <h2 style="margin-bottom: 4px; margin-top: 0px;">OuteAI</h2>
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# Lite-Oute-1-65M

Lite-Oute-1-65M (Base) is an experimental ultra-compact base model in the Lite series, built on the LLaMA architecture and comprising approximately 65 million parameters. <br>
This model is intended as a starting point for fine-tuning on highly specific or narrow tasks. <br>
Due to its extremely small size, this model demonstrates basic text generation abilities but struggle with instructions or maintaining topic coherence.

## Available versions:
<a href="https://huggingface.co/OuteAI/Lite-Oute-1-65M-Instruct">Lite-Oute-1-65M-Instruct</a> <br>
<a href="https://huggingface.co/OuteAI/Lite-Oute-1-65M-Instruct-GGUF">Lite-Oute-1-65M-Instruct-GGUF</a> <br>
<a href="https://huggingface.co/OuteAI/Lite-Oute-1-65M">Lite-Oute-1-65M</a> <br>
<a href="https://huggingface.co/OuteAI/Lite-Oute-1-65M-GGUF">Lite-Oute-1-65M-GGUF</a> <br>

## Benchmarks:
<table style="text-align: left;">
  <tr>
    <th>Benchmark</th>
    <th>5-shot</th>
    <th>0-shot</th>
  </tr>
  <tr>
    <td>ARC Challenge</td>
    <td>21.42</td>
    <td>22.44</td>
  </tr>
  <tr>
    <td>ARC Easy</td>
    <td>38.34</td>
    <td>41.25</td>
  </tr>
  <tr>
    <td>CommonsenseQA</td>
    <td>18.84</td>
    <td>19.49</td>
  </tr>
  <tr>
    <td>HellaSWAG</td>
    <td>28.30</td>
    <td>28.27</td>
  </tr>
  <tr>
    <td>MMLU</td>
    <td>25.44</td>
    <td>23.05</td>
  </tr>
  <tr>
    <td>OpenBookQA</td>
    <td>26.20</td>
    <td>27.60</td>
  </tr>
  <tr>
    <td>PIQA</td>
    <td>60.17</td>
    <td>60.45</td>
  </tr>
  <tr>
    <td>Winogrande</td>
    <td>51.22</td>
    <td>51.70</td>
  </tr>
</table>

## Usage with HuggingFace transformers 
The model can be used with HuggingFace's `transformers` library:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForCausalLM.from_pretrained("OuteAI/Lite-Oute-1-65M").to(device)
tokenizer = AutoTokenizer.from_pretrained("OuteAI/Lite-Oute-1-65M")
def generate_response(message: str, temperature: float = 0.4, repetition_penalty: float = 1.12) -> str:
    # Convert message to PyTorch tensors
    input_ids = tokenizer.encode(
        message, return_tensors="pt"
    ).to(device)
    # Generate the response
    output = model.generate(
        input_ids,
        max_length=256,
        temperature=temperature,
        repetition_penalty=repetition_penalty,
        do_sample=True
    ) 
    # Decode the generated output
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    return generated_text
message = "Scientists have made a breakthrough in renewable energy by developing a new type of"
response = generate_response(message)
print(response)
```

## Risk Disclaimer

By using this model, you acknowledge that you understand and assume the risks associated with its use. You are solely responsible for ensuring compliance with all applicable laws and regulations. We disclaim any liability for problems arising from the use of this open-source model, including but not limited to direct, indirect, incidental, consequential, or punitive damages. We make no warranties, express or implied, regarding the model's performance, accuracy, or fitness for a particular purpose. Your use of this model is at your own risk, and you agree to hold harmless and indemnify us, our affiliates, and our contributors from any claims, damages, or expenses arising from your use of the model.