Llama-3-Taiwan-70B-Instruct
Maintainer: yentinglin
55
🖼️
| 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 Llama-3-Taiwan-70B-Instruct model is a large language model developed by yentinglin that has been finetuned for Traditional Mandarin and English users. It is based on the Llama-3 architecture and demonstrates state-of-the-art performance on various Traditional Mandarin NLP benchmarks. The model was trained using the NVIDIA NeMo Framework on NVIDIA Taipei-1 systems with DGX H100 GPUs. The computing resources and data for training were generously sponsored by several Taiwanese organizations.
The Llama-3-Taiwan-70B-Instruct model has 70 billion parameters and supports both Traditional Mandarin (zh-tw) and English (en). It has been finetuned on a high-quality corpus covering general knowledge as well as industrial domains like legal, manufacturing, medical, and electronics. Key features include an 8K context length and an open model release.
In comparison, the Taiwan-LLaMa-v1.0 model is a smaller 13B parameter model that has also been tailored for Traditional Chinese and Taiwanese cultural contexts. The Llama3-70B-Chinese-Chat model is another large 70B parameter model that has been finetuned for Chinese and English users, with a focus on instruction-following and task-completion capabilities.
Model inputs and outputs
Inputs
- Text: The
Llama-3-Taiwan-70B-Instructmodel takes textual inputs in either Traditional Mandarin or English.
Outputs
- Text: The model generates textual outputs in response to the input, leveraging its broad knowledge and language understanding capabilities.
Capabilities
The Llama-3-Taiwan-70B-Instruct model demonstrates strong capabilities in language understanding, generation, reasoning, and multi-turn dialogue. It can engage in open-ended conversations, answer questions, and complete a variety of language-based tasks, with a focus on Traditional Mandarin and English users. The model's large size and specialized finetuning allow it to excel at tasks requiring in-depth knowledge across multiple domains.
What can I use it for?
The Llama-3-Taiwan-70B-Instruct model can be used for a wide range of applications targeting Traditional Mandarin and English users, such as:
- Chatbots and virtual assistants: The model's conversational and task-completion abilities make it well-suited for building intelligent chatbots and virtual assistants.
- Content generation: The model can be used to generate high-quality text content in Traditional Mandarin and English, such as articles, stories, or product descriptions.
- Language understanding and translation: The model's strong language understanding capabilities can be leveraged for tasks like text classification, sentiment analysis, or machine translation between Traditional Mandarin and English.
- Domain-specific applications: Given the model's finetuning on industry-relevant data, it can be applied to tasks in legal, manufacturing, medical, and electronics domains for users in Taiwan and beyond.
You can try out the Llama-3-Taiwan-70B-Instruct model interactively at the twllm.com demo site or participate in the Chatbot Arena to compete against other chatbots using the model.
Things to try
One interesting aspect of the Llama-3-Taiwan-70B-Instruct model is its ability to seamlessly switch between Traditional Mandarin and English during a conversation, demonstrating a strong grasp of both languages. This makes it well-suited for applications targeting bilingual audiences in Taiwan and beyond.
Another key capability of the model is its high-quality knowledge across a diverse range of domains, from general knowledge to industry-specific topics. This allows users to engage the model in substantive conversations and task completion beyond just open-ended chat.
Overall, the Llama-3-Taiwan-70B-Instruct model represents a significant advancement in large language models tailored for Traditional Mandarin and English users, with the potential to drive innovative applications in Taiwan and globally.
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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