Best Small Language Models (SLMs) with Open source Capabilities

Mistral-7B-v0.1 is a small, yet powerful model adaptable to many use-cases. Mistral 7B is better than Llama 2 13B on all benchmarks, has natural coding abilities, and 8k sequence length. It’s released

BLOOM-560m is a transformer-based language model developed by BigScience, designed to facilitate research in large language models (LLMs). It serves as a pre-trained base model capable of generating h

Granite-3.1-3B-A800M-Base is a state-of-the-art language model developed by IBM, designed to handle complex natural language processing tasks with high efficiency. This model employs a sparse Mixture

Microsoft Azure’s Phi 3 model redefining large-scale language model capabilities in the cloud.

BLOOM-1b1 is a multilingual language model developed by the BigScience Workshop, designed to generate human-like text across 48 languages. As a transformer-based model, it utilizes a decoder-only arch

BLOOM-1b7 is a transformer-based language model developed by the BigScience Workshop, designed to generate human-like text across 48 languages. As a scaled-down variant of the larger BLOOM model, it o

BLOOM-3B is a 3-billion parameter multilingual language model developed by the BigScience initiative. As a scaled-down version of the larger BLOOM model, it maintains the same architecture and trainin

BLOOM-7B1 is a multilingual language model developed by BigScience, designed to generate human-like text across 48 languages. With over 7 billion parameters, it leverages a transformer-based architect

Granite-3.1-1B-A400M-Base is a language model developed by IBM's Granite Team, designed to handle extensive context lengths up to 128K tokens. This model is based on a decoder-only sparse Mixture of E

Granite-3.2-2B-Instruct is a 2-billion-parameter language model developed by IBM's Granite Team, designed to handle a wide range of instruction-following tasks. Built upon its predecessor, Granite-3.1

Granite-3.2-8B-Instruct is an 8-billion-parameter AI model fine-tuned for advanced reasoning tasks. Built upon its predecessor, Granite-3.1-8B-Instruct, it has been trained using a combination of perm

Granite-3.3-2B-Instruct is a 2-billion parameter language model developed by IBM's Granite Team, designed to enhance reasoning and instruction-following capabilities. With a context length of 128K tok

Granite-3.3-8B-Instruct is an advanced language model developed by IBM's Granite Team, featuring 8 billion parameters and a 128K context length. Fine-tuned for enhanced reasoning and instruction-follo

Granite-4.0-Tiny-Preview is a 7-billion-parameter fine-grained hybrid mixture-of-experts (MoE) instruction-following model developed by IBM's Granite Team. Fine-tuned from the Granite-4.0-Tiny-Base-Pr

Granite-4.0-Tiny-Base-Preview is a 7-billion-parameter hybrid mixture-of-experts (MoE) language model developed by IBM's Granite Team. It features a 128,000-token context window and utilizes the Mamba