Key takeaways:
- Gro is a 314 billion parameter mixture of expert model released under Apache 2.0 for commercial use.
- The pre-trained model is uncensored and has a context window of 8,000 tokens.
- The model is resource-intensive, requiring 300 GB of storage space and a DGX H100 with 80 GB VRAM for 8-bit inference.
Gro, a 314 billion parameter mixture of expert model, has been open-sourced under the Apache 2.0 license, allowing for commercial use[1][2]. The pre-trained model is uncensored and has a context window of 8,000 tokens, making it a powerful tool for various applications. However, it's worth noting that the model is resource-intensive, requiring 300 GB of storage space and a DGX H100 with 80 GB VRAM for 8-bit inference[1][2].
Gro outperforms Llama 270 Bill and GPT 3.5 on several benchmarks, showcasing its capabilities. Despite its impressive performance, the question remains whether it's worth using a DGX to run inference on a local machine, given its resource requirements[1][2].
For those interested in using Gro, the process involves cloning the provided GitHub repo, installing the required packages, downloading the weights of the model, and running the python script. The code of conduct encourages users to "be excellent to each other," emphasizing the importance of responsible use[1][2].
In summary, Gro is a significant development in the field of large language models, offering a powerful tool for various applications. However, its resource-intensive nature requires substantial computational resources, making it more suitable for companies and organizations rather than individual users[1][2].
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