Local LLM resource: Mistral Nemo & Large
New, extremely capable small (12B) and large (123B) models released
Mistral's New LLMs: Nemo and Mistral Large 2
French AI startup Mistral AI has recently made waves with the release of two new language models: Mistral Nemo and Mistral Large 2. These models join the ranks of other powerful open-source LLMs like Llama from Meta, which compete with flagship closed models like GPT4o or Claude Sonnet 3.5.
Mistral Nemo: A Powerful yet Accessible Option
Mistral Nemo is a 12.2 billion parameter model released under the Apache license. This licensing choice aligns allows developers extensive freedom to use, modify, and distribute the model for various applications.
Nemo stands out as a drop-in replacement for smaller models like Mistral 7B, offering improved performance without significant increases in computational resources. With Nemo, users can enjoy:
Enhanced capabilities: Nemo's size at 12B enables it to understand and generate more nuanced and contextually relevant responses.
Ease of integration: As a direct replacement for smaller models, Nemo requires minimal adjustments to existing systems.
128k context window: A large context window means Nemo is able to keep an entire book worth of information in a single conversation without losing context
Open-source flexibility: The Apache license allows developers to integrate Nemo into their projects freely, fostering innovation and collaboration.
Mistral Large 2: A Heavyweight Contender
Mistral Large 2 is a 123 billion parameter model released with open weights. While not under an open-source license, the release of model weights enables developers to study, compare, and potentially build upon Mistral Large 2's architecture.
Mistral Large 2's size places it in direct competition with other heavyweight models like Llama 3.1 405B. With this model, users can expect:
State-of-the-art performance: Mistral Large 2 offers impressive capabilities comparable to its peers, making it suitable for complex tasks and applications.
Aimed at professionals: While a 123B parameter sized model is currently (2024-07-25) is too large to host on modern consumer hardware, small businesses and enthusiasts can run it locally much cheaper than Llama3.1 405B, (or on cloud providers) and gain GPT4o like performance for everyday tasks.
