polyglot-ko-1.3b
Maintainer: EleutherAI
71
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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
polyglot-ko-1.3b is a series of large-scale Korean autoregressive language models made by the EleutherAI polyglot team. The 1.3B parameter model consists of 24 transformer layers with a model dimension of 2048, and a feedforward dimension of 8192. It uses Rotary Position Embedding (RoPE) for positional encoding and is trained on 863GB of diverse Korean language data.
Model Inputs and Outputs
Inputs
- Text in Korean
Outputs
- Predicted next token in Korean
Capabilities
polyglot-ko-1.3b is capable of generating coherent and fluent Korean text given a prompt. It demonstrates strong performance on several Korean language understanding benchmarks, outperforming other comparable models like skt/ko-gpt-trinity-1.2B-v0.5 and kakaobrain/kogpt.
What Can I Use It For?
polyglot-ko-1.3b can be used for a variety of Korean language tasks, such as text generation, summarization, translation, and question answering. It could be fine-tuned for specific domains or applications, like generating product descriptions, writing stories, or creating chatbots. However, as with any large language model, the outputs should be carefully curated and filtered before deployment, as the model may generate biased or inappropriate content.
Things to Try
One interesting aspect of polyglot-ko-1.3b is its ability to leverage positional information through the use of Rotary Position Embedding (RoPE). Experimenting with different RoPE configurations or prompts that require strong understanding of context and structure could yield interesting results. Additionally, comparing the performance of polyglot-ko-1.3b to the larger polyglot-ko-12.8b model could provide insights into the benefits of scaling up the model size.
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