TinyLlama-1.1B-intermediate-step-715k-1.5T
Maintainer: TinyLlama
57
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The TinyLlama-1.1B-intermediate-step-715k-1.5T is an intermediate checkpoint of the TinyLlama project, which aims to pretrain a 1.1B Llama model on 3 trillion tokens. With proper optimization, the team plans to achieve this within 90 days using 16 A100-40G GPUs. The training started on September 1, 2023.
Similar models include the TinyLlama-1.1B-step-50K-105b, TinyLlama-1.1B-Chat-v0.6, TinyLlama-1.1B-Chat-v1.0, and the LiteLlama-460M-1T. These models share the same architecture and tokenizer as Llama 2, allowing them to be integrated into many open-source projects.
Model inputs and outputs
The TinyLlama-1.1B-intermediate-step-715k-1.5T model is a text-to-text transformer, meaning it takes text as input and generates text as output.
Inputs
- Text prompt
Outputs
- Generated text
Capabilities
The TinyLlama-1.1B-intermediate-step-715k-1.5T model is capable of a wide range of text generation tasks, such as summarization, translation, and question answering. It can be fine-tuned on specific tasks to further enhance its capabilities.
What can I use it for?
The TinyLlama-1.1B-intermediate-step-715k-1.5T model can be used in a variety of applications that require natural language generation, such as chatbots, content creation, and language learning tools. Due to its compact size of only 1.1B parameters, it can be particularly useful for projects with limited computational resources.
Things to try
One interesting thing to try with the TinyLlama-1.1B-intermediate-step-715k-1.5T model is to explore its few-shot learning capabilities. The model has been pre-trained on a large corpus of text, which may allow it to quickly adapt to new tasks with limited fine-tuning data. Researchers and developers could experiment with different fine-tuning strategies to see how the model's performance scales with the amount of task-specific data.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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