snupilab/AkaLlama-llama3-70b-v0.1
AkaLlama-llama3-70b-v0.1 is a 70 billion parameter Korean language model developed by Yonsei MIRLab, fine-tuned from Meta-Llama-3-70b-Instruct. It is designed for practical usability across a wide range of tasks, with a focus on adapting high-performing LLMs for specific use cases like the Korean language. The model was trained using the Odds Ratio Preference Optimization (ORPO) alignment algorithm on a custom mix of publicly available datasets.
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AkaLlama-llama3-70b-v0.1 Overview
AkaLlama is a series of Korean language models developed by Yonsei MIRLab, with AkaLlama-v0.1 being a 70 billion parameter model fine-tuned from Meta-Llama-3-70b-Instruct. The project aims to explore cost-effective methods for adapting powerful LLMs to specific use cases, particularly for different languages such as Korean, or specialized domains.
Key Capabilities
- Bilingual Support: Designed for both Korean and English language tasks.
- Instruction Following: Fine-tuned with an emphasis on practical usability across various tasks.
- Alignment: Utilizes the Odds Ratio Preference Optimization (ORPO) algorithm for training, similar to HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1.
- Quantized Weights: Available in GGUF and ExLlamaV2 formats for efficient deployment.
Good For
- Applications requiring strong Korean language understanding and generation.
- Developing organization-specific chatbots or domain-specific AI solutions.
- Research into cost-effective LLM adaptation for non-English languages.
Limitations
While powerful, the model's responses can sometimes be inaccurate, biased, or misaligned. The quality of output is also highly dependent on the system prompt and decoding strategy, requiring careful handling and additional testing for reliable use.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.