yaojialzc/Gigi-Llama3-8B-Chinese-zh
Gigi-Llama3-8B-Chinese-zh is an 8 billion parameter Llama-3-8B-Instruct fine-tuned model developed by yaojialzc, specifically enhanced for Chinese-English bilingual tasks. It was trained on over 1.3 million high-quality Chinese-English bilingual corpora, including OpenHermes-2.5, glaive-function-calling, and translated GPT-4 data. This model excels at handling various downstream tasks requiring strong bilingual capabilities, particularly in Chinese contexts.
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Gigi-Llama3-8B-Chinese-zh Overview
Gigi-Llama3-8B-Chinese-zh is the inaugural model in the Gigi series, developed by yaojialzc. It is an 8 billion parameter model based on the Llama-3-8B-Instruct architecture, specifically fine-tuned to significantly enhance its Chinese language capabilities while maintaining strong English performance.
Key Capabilities & Training
- Bilingual Proficiency: Fine-tuned on over 1.3 million high-quality Chinese-English bilingual data points, enabling robust performance in mixed-language contexts.
- Diverse Training Data: Incorporates a wide range of datasets including OpenHermes-2.5 (over 1 million GPT-4 generated instruction-tuning data), glaive-function-calling, refgpt_fact_v2, and extensive Chinese datasets like COIG-CQIA and alpaca-gpt4-data-zh, some translated using GPT-3.5.
- Instruction Following: Designed to handle various downstream tasks effectively, leveraging its instruction-tuned base and specialized fine-tuning.
- Llama-3 Compatibility: Adheres to the Llama-3-8B-Instruct dialogue template, using
<|end_of_text|>as the pad token.
Performance Metrics
Evaluations on the Open LLM Leaderboard show competitive performance:
- Average Score: 67.04
- MMLU (5-Shot): 66.91
- HellaSwag (10-Shot): 80.28
- GSM8k (5-Shot): 66.79
Use Cases
This model is particularly well-suited for applications requiring high-quality Chinese-English bilingual processing and instruction following, making it a strong candidate for tasks in mixed-language environments.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.