Grok-1 is Open Source | All you need to know!!!

Prompt Engineering
17 Mar 202404:00

TLDRElan M has open-sourced Gro, a 314 billion parameter AI model, larger than anticipated, with uncensored content under Apache 2.0. The pre-trained model, based on October 2023 training, requires significant storage and computational resources to run. It outperforms other models on various benchmarks and supports 8-bit quantization. Detailed instructions for running the model on GitHub are provided, emphasizing the community's code of conduct to 'be excellent to each other'.

Takeaways

  • 🚀 Elan M has open-sourced Gro and its weights, releasing the large language model (LLM) that powers charts on the xplatform.
  • 🌐 The weights were released as a torrent file, similar to the approach taken by other AI models.
  • 📈 The model, named Grock 1, is a 314 billion parameter mixture of expert model, significantly larger than expected.
  • 🗓️ The pre-trained model was trained in October 2023, and the release is of the base model, not the fine-tuned version.
  • 📜 Grock 1 is released under the Apache 2.0 license, allowing for commercial use without censorship.
  • 🔢 For each token, only two out of eight experts are active, resulting in approximately 86 billion parameters being active per token.
  • 🧠 The model features 64 layers and 48 attention heads for queries, with an internal embedding size of 6,000.
  • 🛠️ It utilizes rotary embeddings and supports 8-bit quantization as well as activation charging.
  • 💾 To download the model, approximately 300 GB of storage space is required, and running it in 4-bits requires around 320 GB of VRAM.
  • 🏢 Companies may find the model particularly useful due to its size and uncensored nature, which allows for further fine-tuning with good datasets for improved reasoning capabilities.
  • 📊 On four benchmarks (GSM 8K, mmLUU, human evolve, and math), Grock 1 outperforms both the 270 billion parameter Lama model and GPT-3.5.

Q & A

  • What did Elan M announce a week ago?

    -Elan M announced that they would open source Gro and release the weights of Grock 1, the LLM powering charts on the xplatform.

  • How were the weights of Grock 1 released?

    -The weights were released as a torrent file, similar to how Mera AI released their model.

  • What is the size of the Grock 1 model?

    -The Grock 1 model is a 314 billion parameter mixture of expert model.

  • When was the base model of Grock 1 trained?

    -The base model was trained in October 2023.

  • What does the Grock 1 model's release under Apache 2.0 allow for?

    -The release under Apache 2.0 allows for commercial use of the model.

  • What is the significance of the model's 64 layers and 48 attention heads for queries?

    -The 64 layers and 48 attention heads for queries indicate the model's complexity and capacity for handling large-scale computations and understanding context.

  • How much storage space is needed to download the Grock 1 model?

    -Around 300 GB of storage space is needed to download the model.

  • What hardware is recommended to run the Grock 1 model in 4-bits?

    -To run the model in 4-bits, around 320 GB of VRAM is needed, and for 8-bits, a DGX H100 with eight H100s, each having 80 GB of VRAM, is recommended.

  • How can one access and run the Grock 1 model?

    -The model can be accessed and run by cloning the released GitHub repository, installing the required packages with pip, and running the python run.py file after placing everything in the checkpoint directory.

  • What does the code of conduct advise?

    -The code of conduct advises to 'be excellent to each other,' implying a collaborative and respectful approach to working with the model.

  • How does the Grock 1 model perform on benchmarks?

    -The Grock 1 model outperforms the 270 billion parameter Lama model and GPT-3.5 on four benchmarks, including the GSM 8K, mmLUU, human evolve, and math.

  • What potential benefits does the Grock 1 model offer for companies?

    -The Grock 1 model offers potential benefits for companies due to its large size and uncensored nature, which allows for further fine-tuning with good datasets to achieve better results in reasoning and other AI tasks.

Outlines

00:00

🚀 Open Sourcing of Gro and Grock 1 LLM

Elan M announced the open sourcing of Gro and its large language model (LLM), Grock 1, which was released as expected. The model, with 314 billion parameters, is a mixture of expert model, and the pre-trained base model was released under the Apache 2.0 license, allowing commercial use. The model was trained in October 2023 and is uncensored. Technical details include 64 layers, 48 attention heads for queries, an internal embedding size of 6,000, and support for 8-bit quantization and activation charging. The context window supports up to 8,000 tokens. To download and run the model, significant storage and computational resources are required, with 300 GB of storage space and 320 GB of VRAM for 4-bit operation, and a DGX H100 with 8 H100s, each with 80 GB of VRAM for 8-bit operation. A GitHub repository with a Jax example and instructions for running the model was provided, along with a unique code of conduct emphasizing mutual excellence.

Mindmap

Keywords

💡Open Source

Open source refers to something that is publicly accessible and allows others to view, use, modify, and distribute the source code or content without restrictions. In the context of the video, Elan M's decision to open source Gro and its weights signifies a commitment to transparency and collaboration within the tech community. This aligns with the broader theme of promoting innovation through shared resources.

💡Gro

Gro is an AI model developed by Elan M, which is now being open-sourced. It is a large language model (LLM) with 314 billion parameters, designed to power various applications across different platforms. The model's release is significant as it provides a base for further development and fine-tuning by other developers and companies, potentially leading to advancements in AI technology.

💡Weights

In the context of machine learning and AI, weights are the numerical values assigned to model parameters that are adjusted during the training process to minimize the error in the model's predictions. The release of the weights, as mentioned in the script, means that the foundational data and learned patterns of the Gro model are now available for others to use and study, which is a crucial aspect of open-sourcing an AI model.

💡Torrent File

A torrent file is a digital file that contains metadata about files to be downloaded and the location of the trackers. It is associated with the BitTorrent protocol, which is a peer-to-peer (P2P) network used for sharing large files. In the video, the mention of a torrent file for the Gro model's weights indicates a method of distribution that leverages P2P networking to efficiently share the large dataset.

💡Mixture of Expert Model

A mixture of expert (MoE) model is a type of machine learning architecture where a large model is composed of many smaller, specialized models, or 'experts,' each responsible for a particular aspect of the overall task. In the context of the video, Gro is described as a 314 billion parameter MoE model, which suggests that it is designed to handle a wide range of tasks by utilizing the strengths of its many sub-models.

💡Pre-train Model

A pre-trained model is a machine learning model that has undergone an initial phase of training on a large dataset to learn general patterns before it is fine-tuned for a specific task. In the video, the Gro model released is the pre-trained version, meaning it has not been further optimized for any particular application, leaving room for users to fine-tune it according to their needs.

💡Apache 2.0

Apache License 2.0 is a permissive open-source software license that allows users to freely use, modify, and distribute software while providing the copyright holder and contributors with attribution and warranty disclaimers. The mention of Apache 2.0 in the script indicates that the Gro model is released under a license that promotes open collaboration and commercial use without stringent restrictions.

💡Commercial Purposes

Commercial purposes refer to the use of a product, service, or technology in the context of business operations with the aim of generating revenue or profit. In the video, the fact that the Gro model can be used for commercial purposes means that businesses can integrate and modify the model for their own applications without legal or financial barriers, fostering innovation and economic activity.

💡GitHub Repo

A GitHub repository is a storage location for the complete history and files associated with a project, including its source code, documentation, and other related files. In the context of the video, the release of a GitHub repository for the Gro model means that developers have a central location to access the model's code, contribute to its development, and utilize it for their projects.

💡Jax

JAX is a Python library developed by Google for high-performance machine learning research. It provides composable transformations of Python+NumPy functions: differentiate, vectorize, JIT to GPU/TPU, and distribute. In the video, JAX is mentioned as part of the process to run the Gro model, indicating that it is a tool that can be used to execute and potentially optimize the model's performance on various hardware platforms.

💡Checkpoint Directory

A checkpoint directory is a location on a storage system where intermediate states or 'checkpoints' of a machine learning model are saved during the training process. These checkpoints can be used to resume training, perform model evaluation, or recover from failures. In the context of the video, the checkpoint directory is where users are instructed to place the downloaded model weights, indicating it as the storage location for the model's state at the time of release.

Highlights

Elan M announced the open sourcing of Gro and it has been released today.

The release includes the weights of Grock 1, an LLM powering charts on the xplatform.

The weights are distributed as a torrent file, similar to the approach taken by Meta AI.

The model is a 314 billion parameter mixture of expert model, which is larger than expected.

The released model is the pre-trained version, trained in October 2023, and is uncensored.

The model is released under the Apache 2.0 license, allowing for commercial use.

Each token utilizes only two out of eight experts, resulting in approximately 86 billion parameters being active per token.

The model features 64 layers and 48 attention heads for queries, with an internal embedding size of 6,000.

The model uses rotary embeddings and rope tokenizer, similar in size to GPT-4.

Support for 8-bit quantization and activation charging is included in the model.

The maximum sequence length or context window is 8,000 tokens.

Downloading the model requires approximately 300 GB of storage space.

Running the model in 4-bits requires around 320 GB of VRAM.

For 8-bit operation, a DGX H100 with eight H100s, each having 80 GB of VRAM, is needed.

A GitHub repository has been released with a Jax example for running the model.

To run the model, clone the repo, install required packages, and ensure all files are in the checkpoint directory.

The model's code of conduct emphasizes being excellent to each other.

On four benchmarks, Grock 1 outperforms both the 270 billion parameter Lama model and GPT-3.5.

The model's large size and uncensored nature make it a potentially powerful option for companies with good datasets for further fine-tuning.

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