Issue #98: AI21 announces new models
Introducing the Jamba family of models
Welcome to Issue #98 of One Minute AI, your daily AI news companion. This issue discusses a recent announcement about AI21.
AI21 introduces the Jamba 1.5 family of models
AI21 Labs recently introduced the Jamba 1.5 model family, a new line of language models designed to enhance efficiency, accuracy, and versatility in natural language processing tasks. These models are built upon advanced architectures that emphasize scalability and adaptability across various applications, ranging from content creation to complex data analysis. With a focus on reducing latency and improving response times, the Jamba models are tailored to deliver superior performance in real-world scenarios, catering to both enterprise and individual users.
Long context handling: With a 256K effective context window, the longest in the market, Jamba 1.5 models can improve the quality of key enterprise applications, such as lengthy document summarization and analysis, as well as agentic and RAG workflows.
Speed: Up to 2.5X faster on long contexts and fastest across all context lengths in their size class.
Quality: Jamba 1.5 Mini is the strongest open model in its size class, scoring 46.1 on the Arena Hard benchmark, surpassing larger models like Mixtral 8x22B and Command-R+. Jamba 1.5 Large, scoring 65.4, outpaces both Llama 3.1 70B and 405B.
Multilingual: In addition to English, the models support Spanish, French, Portuguese, Italian, Dutch, German, Arabic, and Hebrew.
Developer ready: Jamba natively supports structured JSON output, function calling, digesting document objects, and generating citations.
Open for builders: Both models are available for immediate download on Hugging Face (and coming soon to leading frameworks LangChain and LlamaIndex).
Deploy anywhere: In addition to AI21 Studio, the models are available on cloud partners Google Cloud Vertex AI, Microsoft Azure, and NVIDIA NIM and coming soon to Amazon Bedrock, Databricks Marketplace, Snowflake Cortex, Together.AI as well as for private on-prem and VPC deployment.
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