Otter: A Multi-Modal Model Based on OpenFlamingo

2023/06/22

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Otter is a multi-modal model based on OpenFlamingo, which is an open-sourced version of DeepMind's Flamingo. The model is trained on MIMIC-IT and showcases improved instruction-following and in-context learning ability. Otter is licensed under the MIT license and has gained popularity with 2k stars and 205 forks on GitHub.

The model is designed to be multi-modal, which means it can process information from different modalities such as text, images, and speech. Otter uses a combination of neural networks and reinforcement learning to learn from the environment and improve its performance.

One of the key features of Otter is its ability to follow instructions in context. This means that the model can understand the context of the instruction and perform the task accordingly. For example, if the instruction is to "pick up the red ball," Otter can identify the red ball and pick it up.

Otter has gained popularity among developers due to its ability to learn from the environment and improve its performance over time. Developers can use Otter to build intelligent systems that can interact with humans in a more natural way.

Here's a code snippet that demonstrates how Otter can be used to follow instructions:

from otter import Otter

# Initialize Otter
otter = Otter()

# Follow instructions
instructions = "Pick up the red ball"
result = otter.follow_instructions(instructions)

# Print result
print(result)

In conclusion, Otter is a powerful multi-modal model that can learn from the environment and improve its performance over time. Its ability to follow instructions in context makes it a popular choice among developers who want to build intelligent systems that can interact with humans in a more natural way.