Mistral AI has recently announced the release of Mixtral 8x7B, a new foundation large language model. They reported that this model outperformed GPT3.5 and Llama2 70B models on most benchmarks, which represents a pivotal moment in the LLM landscape that has been dominated so far by closed source models from companies like OpenAI. For the first time, we now have an open-source model that can be deployed in private environments, without compromising on capabilities.
Overview of Mixtral 8x7B
Sparse Mixture-of-Experts (MoE) Model: Mixtral 8x7B is a high-quality sparse mixture-of-experts model. This design allows the model to be both efficient and powerful.
Performance: Mixtral 8x7B boasts impressive performance, outperforming Llama 2 70B on most benchmarks and offering 6x faster inference. It also matches or exceeds GPT3.5 in many standard benchmarks.
Multilingual Capabilities: The model supports multiple languages, including English, French, Italian, German, and Spanish. It also demonstrates strong performance in code generation.
Model Architecture: Mixtral employs a decoder-only model where the feedforward block chooses from a set of 8 distinct groups of parameters. This technique enables the model to control costs and latency effectively, utilizing only a fraction of its total set of parameters per token.
Total Parameters: The model contains 46.7 billion total parameters but uses only 12.9 billion parameters per token.
Technical Advancements and Implications
Efficiency in Model Scaling: The development of Mixtral 8x7B is influenced by research on the efficient scaling of large language models. Recent findings suggest that both model size and training data should grow at a comparable rate to optimize computational efficiency. This approach differs from earlier beliefs that favored increasing model size more significantly than data size.
Optimization for Inference and Cost: A key focus in the development of Mixtral 8x7B has been on optimizing inference time and reducing inference costs, crucial aspects for both developers and users of large language models.
Practical Applications
Code Generation: Mixtral 8x7B shows strong performance in generating code, making it a valuable tool for developers.
Instruction-Following Model: It can be fine-tuned into an instruction-following model, achieving high scores in benchmarks such as MT-Bench.
Open-Source Deployment: Mistral AI has made efforts to integrate Mixtral with a fully open-source stack, making it accessible for widespread use.
Conclusion
Mixtral 8x7B by Mistral AI represents a significant leap forward in the field of large language models, offering high performance, multilingual capabilities, and efficient computational use. Its sparse mixture-of-experts architecture and focus on optimizing inference time and cost make it a valuable addition to the AI community. This model is not just a technological advancement but also a tool that opens up new possibilities for developers and researchers in the field.