IBM continues to increase the variety and performance of its Granite AI LLMs, as shown by Hugging Face benchmark results for the new Granite 3.0 2B and 8B models.
IBM just announced a new collection of AI models, its third generation of Granite LLM s. The cornerstone models of the new collection are the Granite 3.0 2B Instruct and the Granite 3.0 8B Instruct models . The models were trained on over 12 trillion tokens across 12 different human languages and in 116 different programming languages. These models all come with an Apache 2.0 open source license.
AI speculative decoders are also a new IBM offering. Decoders optimize an LLM’s generated text by making guesses about the identification of future tokens. IBM’s speculative decoder called Granite 3.0 8B Accelerator can speed up text generation by as much as 2x during inference. The MoE architecture divides a model into several specialized expert sub-networks for more efficiency. MoE models are small and light, but still considered to be best-in-class for efficiency, with a good balance between cost and power. These models use a small fraction of their total parameters for inference. For example, the 3-billion-parameter MoE model uses only 800 million parameters during inference, and the 1-billion-parameter MoE model uses only 400 million parameters during inference.
LLM IBM IBM Granite LLM Benchmarks Llama LLM Mistral LLM Granite LLM
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