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48094e5c68 Update instruction 2024-03-18 09:09:40 -07:00
1dfcf10e9d Add detailed download instruction 2024-03-18 09:08:14 -07:00
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checkpoints/*
!checkpoints/README.md

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This repository contains JAX example code for loading and running the Grok-1 open-weights model. This repository contains JAX example code for loading and running the Grok-1 open-weights model.
Make sure to download the checkpoint and place the `ckpt-0` directory in `checkpoints` - see [Downloading the weights](#downloading-the-weights) Make sure to download the checkpoint and place `ckpt-0` directory in `checkpoint` - see [Downloading the weights](Downloading-the-weights)
Then, run Then, run
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The script loads the checkpoint and samples from the model on a test input. The script loads the checkpoint and samples from the model on a test input.
Due to the large size of the model (314B parameters), a machine with enough GPU memory (314GB+ vRAM) is required to test the model with the example code. 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.
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. 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.
# Model Specifications
Grok-1 is currently designed with the following specifications:
- **Parameters:** 314B
- **Architecture:** Mixture of 8 Experts (MoE)
- **Experts Utilization:** 2 experts used per token
- **Layers:** 64
- **Attention Heads:** 48 for queries, 8 for keys/values
- **Embedding Size:** 6,144
- **Tokenization:** SentencePiece tokenizer with 131,072 tokens
- **Additional Features:**
- Rotary embeddings (RoPE)
- Supports activation sharding and 8-bit quantization
- **Maximum Sequence Length (context):** 8,192 tokens
# Downloading the weights # Downloading the weights
You can download the weights using a torrent client and this magnet link: You can download the weights using a torrent client and this magnet link:
``` ```
magnet:?xt=urn:btih:5f96d43576e3d386c9ba65b883210a393b68210e&tr=https%3A%2F%2Facademictorrents.com%2Fannounce.php&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounce magnet:?xt=urn:btih:5f96d43576e3d386c9ba65b883210a393b68210e&tr=https%3A%2F%2Facademictorrents.com%2Fannounce.php&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounce
``` ```
or directly using [HuggingFace 🤗 Hub](https://huggingface.co/xai-org/grok-1): or directly using HuggingFace:
``` ```
git clone https://github.com/xai-org/grok-1.git && cd grok-1 git clone https://github.com/xai-org/grok-1.git && cd grok-1
pip install huggingface_hub[hf_transfer] pip install huggingface_hub[hf_transfer]

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dm_haiku==0.0.12 dm_haiku==0.0.12
jax[cuda12-pip]==0.4.25 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html jax[cuda12_pip]==0.4.25 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
numpy==1.26.4 numpy==1.26.4
sentencepiece==0.2.0 sentencepiece==0.2.0