In this video we dive into the Mistral 7b paper.
We discuss the release of a new open-source large language model called Mistral 7B by Mistral AI, a French company. With 7.3 billion parameters, the model is smaller than recent models and is licensed under the Apache 2.0 license, allowing commercial use. It shows promising performance compared to larger models, such as the Llama family.
Mistral 7B uses a Sliding Window Attention mechanism to manage memory and reduce inference time. It also employs Group Query Attention to reduce memory usage while maintaining performance. The model's performance is evaluated using the MMLU (general knowledge) and GSM 8K (high school math problems) benchmarks, where it outperforms larger models.
The model is available on Hugging Face, and fine-tuning options are offered by Mistral AI and the community. The OpenOrca fine-tuning is based on the OpenOrca dataset, which focuses on detailed reasoning, and the results show significant improvement over larger models.
Comparing Mistral 7B's performance to larger models, it is evident that smaller models can achieve impressive results without the need for extensive infrastructure or tooling. We emphasize the potential of prompt engineering and few-shot prompts to improve model performance without significant additional costs.
In conclusion, the discussion highlights the potential of smaller language models and the impact of prompt engineering and few-shot prompts on model performance. It also emphasizes the importance of evaluating and fine-tuning models for specific use cases.
