Yi-1.5-34B
Yi-1.5 is a groundbreaking AI model, pushing the boundaries of what’s possible in the realm of artificial intelligence. It’s been trained on an enormous corpus of 500 billion tokens, encompassing a wide array of fields and disciplines. This extensive pre-training has equipped Yi-1.5 with a comprehensive understanding of language, context, and knowledge.
Beyond language comprehension, Yi-1.5 excels in tasks involving coding, math, reasoning, and instruction-following, thanks to fine-tuning on 3 million diverse samples. Whether you’re a software developer automating coding tasks, a researcher analyzing complex datasets, or an educator creating interactive learning experiences, Yi-1.5 is ready to assist.
What sets Yi-1.5 apart is its adaptability. The model can be further fine-tuned for specific tasks or domains, making it a versatile tool for a broad range of applications. With Yi-1.5, the future of AI is here and now, ready to be explored and utilized.
Comparison
General Performance
- MMLU: Yi-1.5-34B-Chat shows a score of 76.8. Other models like Qwen1.5-32B-Chat, Qwen1.5-72B-Chat, Mixtra1-Bx78-Instruct-v0.1, and Mixtra1-Bx228-Instruct-v0.1 scored 74.3, 77.3, 71.4, and 77.7 respectively.
- GSM8K: Yi-1.5-34B-Chat scores 90.2, outperforming all other models.
- MATH: Yi-1.5-34B-Chat scores 50.1, leading the pack with the highest score.
- HumanEval: Yi-1.5-34B-Chat achieves 75.2, only surpassed by Mixtra1-Bx228-Instruct-v0.1 with 76.2.
- MBPP: Yi-1.5-34B-Chat reaches 74.6, again only surpassed by Mixtra1-Bx228-Instruct-v0.1 with 73.8.
- MT-Bench: Yi-1.5-34B-Chat and Qwen1.5-72B-Chat both score 8.5, the highest in this category.
- Alignbench: Yi-1.5-34B-Chat achieves 7.2, tied with Qwen1.5-72B-Chat for the highest score.
- ArenaHard: Yi-1.5-34B-Chat scores 42.6, the highest in this category.
- AlpacaEval2.0: Yi-1.5-34B-Chat and Qwen1.5-72B-Chat both score 36.6, the highest in this category.
Highlights:
- Yi-1.5-34B-Chat consistently performs well across all categories, often scoring the highest or near the top.
- The most significant lead is observed in the GSM8K category, where Yi-1.5-34B-Chat outperforms all other models.
- Yi-1.5-34B-Chat shows strong performance in knowledge reasoning and reading comprehension tasks, indicating a substantial advancement over other models.
| Benchmark | Yi-1.5-34B-Chat | Qwen1.5-32B-Chat | Qwen1.5-72B-Chat | Mixtral-8x7B-Instruct-v0.1 | Mixtral-8x22B-Instruct-v0.1 |
|---|---|---|---|---|---|
| MMLU | 76.8 | 74.3 | 77.3 | 71.4 | 77.7 |
| GSM8K | 90.2 | 83.9 | 86.0 | 65.7 | 84 |
| MATH | 50.1 | 43.3 | 44.4 | 30.7 | 41.1 |
| HumanEval | 75.2 | 51.2 | 64.6 | 45.1 | 76.2 |
| MBPP | 74.6 | 66.9 | 72.5 | 59.5 | 73.8 |
| MT-Bench | 8.5 | 8.3 | 8.6 | 8.3 | 8.6 |
| Alignbench | 7.2 | 7.1 | 7.2 | 5.7 | 6.5 |
| ArenaHard | 42.6 | 24.2 | 36.1 | 23.4 | 36.4 |
| AlpacaEval2.0 | 36.6 | 23.9 | 36.6 | 23.7 | 30.9 |
Team
The team behind the LLM model is a diverse group of highly skilled individuals, each bringing their unique expertise to the table. The team comprises data scientists, machine learning engineers, and researchers who have extensive experience in the field of artificial intelligence. They have backgrounds in various disciplines such as computer science, mathematics, and linguistics, which allows them to approach the development of the LLM model from multiple perspectives. The team’s collaborative efforts have resulted in the creation of a model that is capable of understanding and generating human-like text, making significant strides in the field of natural language processing. Their dedication to continuous learning and improvement ensures that the LLM model remains at the forefront of AI technology. The team’s work is guided by a commitment to ethical AI practices, ensuring that the LLM model is used responsibly and benefits a wide range of users. Their collective expertise and dedication have been instrumental in the successful development and deployment of the LLM model. They continue to innovate and push the boundaries of what is possible in AI technology.
Community
Community engagement through various channels such as GitHub, Discord, WeChat, and the Yi Learning Hub. It also invites users to contribute to the model’s development and participate in discussions, reflecting a strong emphasis on collaborative improvement and open-source ethos.