---
license: apache-2.0
tags:
- openvino
- openvino-export
pipeline_tag: text-generation
base_model: OuteAI/Lite-Oute-1-300M-Instruct
---

This model was converted to OpenVINO from [`OuteAI/Lite-Oute-1-300M-Instruct`](https://huggingface.co/OuteAI/Lite-Oute-1-300M-Instruct) using [optimum-intel](https://github.com/huggingface/optimum-intel)
via the [export](https://huggingface.co/spaces/echarlaix/openvino-export) space.

# Lite-Oute-1-300M-Instruct

Lite-Oute-1-300M-Instruct is a Lite series model based on the Mistral architecture, comprising approximately 300 million parameters. <br>
This model aims to improve upon our previous 150M version by increasing size and training on a more refined dataset. The primary goal of this 300 million parameter model is to offer enhanced performance while still maintaining efficiency for deployment on a variety of devices. <br>
With its larger size, it should provide improved context retention and coherence, however users should note that as a compact model, it still have limitations compared to larger language models. <br>
The model was trained on 30 billion tokens with a context length of 4096.

## Available versions:
<a href="https://huggingface.co/OuteAI/Lite-Oute-1-300M-Instruct">Lite-Oute-1-300M-Instruct</a> <br>
<a href="https://huggingface.co/OuteAI/Lite-Oute-1-300M-Instruct-GGUF">Lite-Oute-1-300M-Instruct-GGUF</a> <br>
<a href="https://huggingface.co/OuteAI/Lite-Oute-1-300M">Lite-Oute-1-300M</a> <br>
<a href="https://huggingface.co/OuteAI/Lite-Oute-1-300M-GGUF">Lite-Oute-1-300M-GGUF</a> <br>

## Chat format
> [!IMPORTANT]
> This model uses **ChatML** template. Ensure you use the correct template:

```
<|im_start|>system
[System message]<|im_end|>
<|im_start|>user
[Your question or message]<|im_end|>
<|im_start|>assistant
[The model's response]<|im_end|>
```

## 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>26.37</td>
    <td>26.02</td>
  </tr>
  <tr>
    <td>ARC Easy</td>
    <td>51.43</td>
    <td>49.79</td>
  </tr>
  <tr>
    <td>CommonsenseQA</td>
    <td>20.72</td>
    <td>20.31</td>
  </tr>
  <tr>
    <td>HellaSWAG</td>
    <td>34.93</td>
    <td>34.50</td>
  </tr>
  <tr>
    <td>MMLU</td>
    <td>25.87</td>
    <td>24.00</td>
  </tr>
  <tr>
    <td>OpenBookQA</td>
    <td>31.40</td>
    <td>32.20</td>
  </tr>
  <tr>
    <td>PIQA</td>
    <td>65.07</td>
    <td>65.40</td>
  </tr>
  <tr>
    <td>Winogrande</td>
    <td>52.01</td>
    <td>53.75</td>
  </tr>
</table>







First make sure you have optimum-intel installed:

```bash
pip install optimum[openvino]
```

To load your model you can do as follows:

```python
from optimum.intel import OVModelForCausalLM

model_id = "FM-1976/Lite-Oute-1-300M-Instruct-openvino"
model = OVModelForCausalLM.from_pretrained(model_id)
```