project
Grok-1 - A large open-source model from Elon Musk's xAI, with 314 billion parameters.
Grok-1 is a large-scale language model developed by xAI, an AI startup founded by Elon Musk. It's a hybrid expert (MoE) model with 314 billion parameters, making it the largest open-source language model in terms of parameter count...
What is Grok-1?
Grok-1 is a large-scale language model developed by xAI, an AI startup founded by Elon Musk. It is a hybrid expert (MoE) model with 314 billion parameters, making it the largest open-source language model in terms of parameters. Grok-1's development and training followed open-source principles; its weights and network architecture are publicly available under the Apache 2.0 license, allowing users to freely use, modify, and distribute it for personal and commercial purposes.
Grok-1 official website entrance
- Official article introduction:https://x.ai/blog/grok-os
- GitHub address:https://github.com/xai-org/grok-1
- Hugging Face Address:https://huggingface.co/xai-org/grok-1
- Model weight download:
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Grok-1 model information
According to xAI's official model introduction (https://x.ai/model-card/The information for Grok-1 is as follows:
| project | Detailed information |
|---|---|
| Model details | Grok-1 is a Transformer-based autoregressive model pre-trained for next token prediction. The model was fine-tuned using extensive feedback from humans and the earlier Grok-0 model. The initial version of Grok-1 had a context length of 8192 tokens and was released in November 2023. |
| Intended Use | Grok-1 is designed to serve as the engine behind the Grok chatbot for natural language processing tasks including question answering, information retrieval, creative writing, and coding assistance. |
| limitation | While Grok-1 excels in information processing, human review of its work is necessary to ensure accuracy. Grok-1 language models lack the ability to independently search the network. When deployed within Grok, search tools and databases enhance the model's capabilities and factuality. Even with access to external information sources, the model may still produce illusions. |
| Training data | The training data used in the Grok-1 release comes from the internet and data provided by AI trainers at xAI up to the third quarter of 2023. |
| Evaluate | Grok-1 has been evaluated on a range of reasoning benchmark tasks and a selection of foreign math exam problems. xAI has reached out to early alpha testers to evaluate the Grok-1 version, including adversarial testing. xAI is expanding our early adopter pool with Grok Early Access in preparation for closed beta testing. |
Technical details of Grok-1
- Base model and trainingGrok-1 is trained on a large amount of text data without being fine-tuned for any specific task. This means it is a general-purpose language model that can be applied to a variety of different natural language processing tasks. Its training is done from scratch using a custom training stack consisting of the JAX library and the Rust language.
- Number of parametersGrok-1 has 314 billion parameters, making it the largest open-source language model in terms of parameter count. The activation weight of these parameters on a given token is 25%, indicating the model's scale and complexity.
- Hybrid Expert Model (MoE)Grok-1 employs a hybrid expert system design, which combines multiple expert networks to improve model efficiency and performance. In Grok-1, each token is processed by selecting two experts from eight experts.
- Activation parametersGrok-1 has 86 billion activation parameters, more than Llama-2's 70B, indicating its potential for language tasks.
- Embedding and Position EmbeddingGrok-1 uses rotational embeddings instead of fixed-position embeddings, a method for handling sequential data that improves the model's ability to process long texts. The tokenizer vocabulary size is 131,072, similar to GPT-4, and the embedding size is 6,144.
- Transformer layerThe model contains 64 Transformer layers, each with a decoder layer, consisting of multi-head attention blocks and dense blocks. The multi-head attention blocks have 48 heads for queries and 8 heads for key/value pairs (KV), with a KV size of 128. The dense blocks (dense feedforward blocks) have a widening factor of 8, and the hidden layer size is 32,768.
- QuantificationGrok-1 also provides some 8-bit quantization for the weights, which helps reduce the model's storage and computational requirements, making it more suitable for running in resource-constrained environments.
- Operating requirementsBecause Grok-1 is a large model (314B parameters), it requires a machine with sufficient GPU memory to run. It is estimated that a machine with 628GB of GPU memory (2 bytes per parameter) may be needed.