- author: AI FOCUS
Introducing MPT-30B: The New King of Open Source
In the world of open source models, a new king has emerged from Mosaic ML. It goes by the name MPT-30B, and it is revolutionizing the field. This open source model, which is licensed for commercial use, outperforms the renowned GPT-3. Trained on Nvidia H100s, MPT-30B boasts an impressive context length of 8,000 tokens, surpassing most other open source models.
The Mosaic ML's MPT Family
Mosaic ML is the mastermind behind the MPT family of models. MPT stands for "Mosaic pre-trained Transformer," and it is a decoder-only Transformer in the style of GPT. What sets MPT apart is its faster speed, greater stability, and ability to handle larger context lengths. Prior to MPT-30B, Mosaic ML released the MPT-7B model, which garnered over 3 million downloads. The success of MPT-7B showcased the potential of the MPT models, leading to further innovations.
MPT-30B: The Crown Jewel
Mosaic ML has now presented us with MPT-30B, accompanied by two fine-tuned variants: MPT-30B Instruct and MPT-30B Chat. These models are specifically designed for single-turn instruction following and multi-turn conversations, respectively. Let's delve into the remarkable features that set MPT-30B apart from the competition.
Unleashing the Power of Context
A standout attribute of MPT-30B is its context length. With a staggering 8,000-token capacity, it surpasses the limited context lengths of other models. Moreover, thanks to Alibi, this context length can be expanded further. This capability to handle immense amounts of contextual information empowers users to delve deeper into complex tasks.
Unleashing the Power of Hardware
MPT-30B also possesses an advantage in its training process. It is the first open source model trained on the Nvidia H100s, a testament to the cutting-edge hardware it leverages. This training on top-of-the-line technology translates to exceptional performance. Additionally, MPT-30B's optimal size allows it to run smoothly on a single GPU, making it more accessible and cost-effective compared to similar models like Falcon 40B.
Core Capabilities and Comparisons
The base model, MPT-30B, delivers superior performance compared to GPT-3 and remains highly competitive with models such as Falcon 40B and Lama 30B. With a training process that encompasses sequencing 1 trillion tokens and subsequent training on 50 billion tokens, MPT-30B excels in both excellence and scale.
Mosaic ML conducted a thorough evaluation of six core capabilities, including programming, systematic problem solving, common sense reasoning, reading comprehension, language understanding, and world knowledge. MPT-30B surpassed its predecessor, MPT-7B, in all categories. While it lags behind Lama 30B and Falcon 40B in text capabilities, it truly shines in programming. The pre-training data mix rich in code allows MPT-30B to outperform other models in this significant area.
A Comparison to Existing Models
To offer a comprehensive comparison, researchers at Mosaic ML evaluated the human evaluation score of every MPT-30B model alongside existing open source models, particularly those focused on code generation. Impressively, all MPT-30B models exhibit strong coding capabilities, with the MPT-30B Chat model surpassing all models except for Wizard Coder.
Outperforming GPT-3 with Efficiency
MPT-30B not only outperforms GPT-3 in various metrics but also does so with only 17% of GPT-3's parameters. This remarkable achievement speaks volumes about the advancements in model training and the efficiency of MPT-30B's architecture. It serves as an indicator of the rapid progress made in the field of AI and offers a glimpse into the promising future that lies ahead.
Expanding the MPT Family: Instruct and Chat Versions
In addition to the exceptional base model, Mosaic ML introduced two fine-tuned variants to fulfill specialized tasks. MPT-30B Instruct is trained on diverse sources such as Anthropic Dali, Spider, and grade school math datasets. The goal is to enable the model to follow instructions effectively, reducing the need for intricate prompt engineering tricks.
MPT-30B also brings us the conversational version of the model: MPT-30B Chat. This variant is fine-tuned on massive chat datasets, totaling 1.54 billion tokens. It utilizes the chat ML format to prevent malicious prompt injections. While not intended for commercial use, MPT-30B Chat demonstrates the potent combination of MPT-30B and large, high-quality fine-tuned datasets.
As we delve into the capabilities of MPT-30B through practical tests, we can truly understand the impact and potential of this open source model.
The MPT-30b Chat Model: A Promising Advancement in AI
The field of artificial intelligence (AI) is constantly evolving, with new models pushing the boundaries of what machines can do. One such model that has caught our attention is the MPT-30b Chat Model. In this article, we will delve into its capabilities and discuss its potential to compete with industry leader OpenAI.
Refining Logical Reasoning and Inference
The MPT-30b Chat Model has proven its prowess in logical reasoning and inference, providing insightful answers to complex questions. For instance, when presented with a series of statements comparing the cost of blueberries, strawberries, and raspberries, the model was able to accurately determine the true statements. Its ability to reason and arrive at correct answers showcases the advancements made in natural language understanding.
Customization and Data Privacy
An advantage of the MPT-30b Chat Model is the flexibility it offers in terms of customization and data privacy. Mosaic's inference software stack allows users to serve these models on Mosaic Hardware or their own private hardware. Moreover, users can fine-tune or train the model with their private data, ensuring that sensitive information remains confidential and ownership of the final model is retained.
Starter and Enterprise Tiers: Choose What Suits You
Depending on your requirements and preferences, the MPT-30b Chat Model provides two tiers: Starter and Enterprise. The Starter tier enables users to utilize the model's API for building their own applications at a significantly lower cost compared to OpenAI's API. This tier guarantees comparable quality while minimizing expenses. On the other hand, for users seeking maximum cost efficiency, model accuracy, and data privacy, the Enterprise tier allows for deploying fine-tuned models in a dedicated virtual cloud. With pricing based on GPU minutes rather than tokens, the Enterprise tier proves to be highly cost-efficient.
A Glimpse into the Future
As the AI landscape continues to evolve, open source models are constantly pushing the boundaries of what is possible. While the MPT-30b Chat Model presents a promising advancement, it is clear that there is still room for improvement. Open source models, including those developed by OpenAI, consistently strive to outperform each other, driving progress and innovation within the field.
Will there come a day when open source models can compete head-to-head with the best OpenAI has to offer? As we ponder this question, it is evident that there is fierce competition in the AI community, with different organizations vying to unlock the secret sauce of AI dominance. It will be fascinating to witness how this competition unfolds and whether open source models can challenge the status quo.
For more AI-related content, make sure to watch the video on the MPT-7b Story Rider model that successfully wrote an entire epilogue to "The Great Gatsby." Thank you for visiting AI Focus, and we look forward to your comments on the future of open source AI models.