Colossal-LLaMA-2-7b-base
Maintainer: hpcai-tech
75
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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 Colossal-AI team has introduced the open-source model Colossal-LLaMA-2-7B-base. This model, a derivation of LLaMA-2, has undergone continual pre-training involving approximately 8.5 billion tokens over a duration of 15 hours with 64 A800 GPUs. At a cost of less than $1,000, you can achieve results similar to those that cost millions of dollars to pretrain from scratch. It is licensed under the LLaMA-2 license and Apache 2.0 License without any additional commercial use restrictions.
Colossal-LLaMA-2-7B-base is designed to accommodate both the Chinese and English languages, featuring an expansive context window spanning 4096 tokens. It has exhibited exceptional performance when benchmarked against models of equivalent scale in standard Chinese and English evaluation metrics, including C-Eval and MMLU.
Model Inputs and Outputs
Inputs
- Text: The model accepts text input that can be used to generate coherent and contextually relevant output.
Outputs
- Text: The model generates text output that continues or expands upon the provided input.
Capabilities
Colossal-LLaMA-2-7B-base has demonstrated strong performance on a variety of tasks, including language understanding, reasoning, and generation. It has shown competitive results compared to larger and more expensive models, making it a cost-effective solution for building domain-specific or task-focused models.
What can I use it for?
The Colossal-LLaMA-2-7B-base model can be used as a foundation for building a wide range of natural language processing applications, such as language generation, question-answering, and dialogue systems. Its broad language understanding capabilities and low-cost pretraining make it an attractive option for researchers and developers looking to build custom models for specific domains or use cases.
Things to try
One interesting aspect of the Colossal-LLaMA-2-7B-base model is its ability to handle both Chinese and English languages. Developers could explore ways to leverage this cross-lingual capability, such as building multilingual applications or models that can seamlessly switch between the two languages. Additionally, the model's large context window of 4096 tokens opens up possibilities for exploring long-form text generation or summarization tasks.
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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