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README.md
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README.md
@ -4,21 +4,27 @@ This repository contains JAX example code for loading and running the Grok-1 ope
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Make sure to download the checkpoint and place the `ckpt-0` directory in `checkpoints` - see [Downloading the weights](#downloading-the-weights)
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Make sure to download the checkpoint and place the `ckpt-0` directory in `checkpoints` - see [Downloading the weights](#downloading-the-weights)
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Then, run
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## 1. Installation
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```shell
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1. Install the project dependencies
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```bash
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pip install -r requirements.txt
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pip install -r requirements.txt
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```
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2. Run the project
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```bash
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python run.py
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python run.py
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```
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```
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to test the code.
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The script loads the checkpoint and samples from the model on a test input.
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The script loads the checkpoint and samples from the model on a test input.
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Due to the large size of the model (314B parameters), a machine with enough GPU memory is required to test the model with the example code.
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Due to the large size of the model (314 Billion parameters), a machine with enough GPU memory is required to test the model with the example code.
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The implementation of the MoE layer in this repository is not efficient. The implementation was chosen to avoid the need for custom kernels to validate the correctness of the model.
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The implementation of the MoE layer in this repository is not efficient. The implementation was chosen to avoid the need for custom kernels to validate the correctness of the model.
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# Model Specifications
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## 2. Model Specifications
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Grok-1 is currently designed with the following specifications:
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Grok-1 is currently designed with the following specifications:
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@ -33,8 +39,9 @@ Grok-1 is currently designed with the following specifications:
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- Rotary embeddings (RoPE)
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- Rotary embeddings (RoPE)
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- Supports activation sharding and 8-bit quantization
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- Supports activation sharding and 8-bit quantization
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- **Maximum Sequence Length (context):** 8,192 tokens
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- **Maximum Sequence Length (context):** 8,192 tokens
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- **TPU/GPU:** NVIDIA/AMD supported only
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# Downloading the weights
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## 3. Downloading the weights
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You can download the weights using a torrent client and this magnet link:
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You can download the weights using a torrent client and this magnet link:
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@ -1,4 +1,4 @@
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dm_haiku==0.0.12
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dm-haiku==0.0.12
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jax[cuda12_pip]==0.4.25 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
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jax[cuda12-pip]==0.4.25 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
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numpy==1.26.4
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numpy==1.26.4
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sentencepiece==0.2.0
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sentencepiece==0.2.0
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