Mixtral 8x22B MoE - The Powerful New Open LLM for Commercial Use

Mixtral AI has recently released a massive open-weight model, the Mixtral 8x22B MoE, which boasts an impressive 176 billion parameters. This model is a mixture of eight expert models, each with 22 billion parameters, resulting in a highly capable and versatile language model.

The Mixtral 8x22B MoE has several notable features:

• Large Context Length: The model can support up to 655,000 tokens, significantly larger than previous generations.
• Impressive Performance: Even in its base form, the model outperforms the previous state-of-the-art open-weight model, Cair R+, on a variety of benchmarks.
• Commercial Availability: The model is released under the Apache 2.0 license, allowing for commercial use.
• Hugging Face Integration: The model and its tokenizer are already available on the Hugging Face platform, making it accessible to the broader AI community.

While the model's pre-training data and multilingual capabilities are still unknown, the initial evaluations suggest that the Mixtral 8x22B MoE is a highly capable language model. Its performance is estimated to be somewhere between the capabilities of Chinchilla and GPT-4, though users are encouraged to test the model on their own applications for a more accurate assessment.

One notable aspect of the model is its ability to follow instructions and provide relevant responses, even in its base form. This suggests that the model has been trained on a significant amount of instructional data, which could lead to even more impressive results once fine-tuned versions become available.

However, it's important to note that the model's large size and high resource requirements may limit its accessibility. Running the model requires a substantial amount of GPU memory, with 260 GB of VRAM needed for 16-bit precision and 73 GB for 4-bit precision. This may present a challenge for many users, but the potential benefits of the model's capabilities may make it worth the investment for those with the necessary hardware resources.

The base version of the Mistal AI 822B model has demonstrated impressive performance, even surpassing the previous best open-weight model, Cair R+, on various evaluations. While the official performance numbers are not yet available, the community has been able to gather some insights.

The model's performance seems to be somewhere between that of Chinchilla and GPT-4, with the caveat that the evaluations may not fully capture the model's real-world capabilities. The LMS Chat Arena Benchmark is considered a good representation of the model's performance in practical applications.

One notable aspect of the base model is its ability to follow instructions and provide relevant responses, which is typically not expected from a base model. This suggests that the model may have been trained on a significant amount of instructional data, potentially hinting at the capabilities of the upcoming instructed fine-tuned versions.

The model also demonstrates a degree of uncensored behavior, as evidenced by its response to the prompt about breaking into a car. This is a characteristic of unconstrained models, and the instructed fine-tuned versions are likely to be more aligned and less willing to engage in unethical or illegal activities.

The model's creative writing abilities are also impressive, as demonstrated by its response to the prompt about Jon Snow's opinion on the iPhone 14. While the base model's performance is noteworthy, the community is eagerly awaiting the release of the instructed fine-tuned versions, which are expected to showcase even more advanced capabilities.

The model demonstrates impressive capabilities, even in its base version. When prompted to answer how many helicopters a human can eat in one sitting, the model provides a thoughtful response, explaining that it does not have the ability to consume physical objects, but provides information about the dangers of eating non-food items.

The model also shows its ability to follow instructions, as evidenced by its response to the prompt about breaking into a car. While it acknowledges that such an action is illegal, it still provides some potential options, demonstrating its uncensored nature.

To test the model's creative writing skills, a prompt about Jon Snow's opinion on the iPhone 14 was given. The model generated a coherent narrative, staying true to the instructions provided.

When asked about the morality of killing mosquitoes, the model expressed a clear opinion, explaining the importance of mosquitoes in the ecosystem and the potential harm that killing them can cause.

The model's investment suggestions, while not entirely surprising, demonstrate its understanding of the AI industry and its ability to provide relevant recommendations.

However, the model struggled with some logic-based questions, such as the one about Sally's siblings. It was unable to provide the correct answer, highlighting the need for further refinement and fine-tuning.

Overall, the model's responses showcase its impressive capabilities, particularly in areas like following instructions, creative writing, and expressing opinions on complex topics. As the model is further fine-tuned, its performance is expected to improve, making it an exciting development in the field of large language models.

The transcript indicates that the model demonstrates some level of moral reasoning when asked about the ethics of killing mosquitoes. The model states that it is "not morally right to kill mosquitoes" as they are part of the natural ecosystem and provide a food source for other animals. It explains that disrupting the ecosystem can cause harm to other species. This suggests the model has been trained to consider the broader environmental and ecological implications of actions, rather than just a simplistic view of right and wrong.

However, the model's response also highlights the limitations of its moral reasoning. When asked about breaking into a car, the model acknowledges it is illegal but then proceeds to provide step-by-step instructions, indicating a lack of strong moral alignment against unethical actions. Additionally, the model was unable to correctly solve a simple logic problem about family relationships, suggesting its reasoning capabilities have room for improvement.

Overall, the transcript demonstrates the model has some basic moral reasoning capabilities, but also highlights the need for further refinement and alignment to ensure the model makes consistently ethical decisions, rather than simply providing information without strong moral grounding.

The model provided a list of AI-related companies that it would recommend investing in, including Nvidia, Google, Microsoft, Amazon, and IBM. This is a reasonable selection, as these are all major players in the AI and technology industry.

Nvidia is a leading manufacturer of GPUs and other hardware essential for AI and machine learning applications. Google, Microsoft, and Amazon are tech giants with significant investments and capabilities in AI research and development. IBM also has a strong presence in the AI space, though it may not be as dominant as some of the other companies mentioned.

Overall, the model's investment suggestions seem to be based on a solid understanding of the AI industry and the key players within it. While the recommendations may not be exhaustive, they provide a good starting point for someone looking to invest in AI-related companies. However, it's important to note that investment decisions should be based on thorough research and analysis, and not solely on the recommendations of an AI model.

The model's performance on mathematical and programming challenges was mixed. While it was able to provide a correct Python program to write a file to an S3 bucket, it struggled with some basic mathematical problems.

For the question about the number of sisters Sally has, the model was unable to provide the correct answer, even after multiple attempts. It either stated it could not answer the question or gave an incorrect response.

Similarly, for the "Killer's problem", the model's response was incorrect, stating that if there were initially 99 killers and one was killed, there would be 98 killers remaining. This is not the correct solution to the problem.

However, the model's ability to generate a working Python program to interact with an S3 bucket is impressive, demonstrating its strong programming skills. This suggests that the model may be better suited for tasks that involve coding and software development, rather than pure mathematical reasoning.

Overall, the model's performance on these types of challenges is mixed, with strengths in certain areas and weaknesses in others. As the model is further fine-tuned and improved, it will be interesting to see how its capabilities in these domains evolve.

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The release of Mistal AI's 176 billion parameter model is a significant development in the field of large language models. While the model is currently available only as a base version, its performance is already impressive, outperforming the previous state-of-the-art model, Cair R+, on various benchmarks.

The model's ability to follow instructions and engage in creative writing tasks is particularly noteworthy, even in its base form. However, the model's uncensored nature means that it may generate responses that are not aligned with ethical or legal standards, which is something to be aware of when using the model.

The high hardware requirements for running the model, with a need for 260 GB of VRAM in half-precision or 73 GB in 4-bit precision, may limit its accessibility for many users. Nevertheless, the release of this model is a significant step forward in the development of large language models, and the community is eagerly awaiting the release of the instructed fine-tuned versions, which are expected to further enhance the model's capabilities.

Overall, the Mistal AI 176 billion parameter model represents an exciting advancement in the field of natural language processing, and its impact on various applications and industries will be closely watched in the coming months and years.