AI21-Jamba-Mini-1.5
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AI21-Jamba-Reasoning-3B
ai21labsAI21’s Jamba Reasoning 3B is a top-performing reasoning model that packs leading scores on intelligence benchmarks and highly-efficient processing into a compact 3B build. Read the full blog post here. The hybrid design combines Transformer attention with Mamba (a state-space model). Mamba layers are more efficient for sequence processing, while attention layers capture complex dependencies. This mix reduces memory overhead, improves throughput, and makes the model run smoothly on laptops, GPUs, and even mobile devices, while maintainig impressive quality. **Smart: Leading intelligence scores** The model outperforms competitors, such as Gemma 3 4B, Llama 3.2 3B, and Granite 4.0 Micro, on a combined intelligence score that averages 6 standard benchmarks.
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View details ↗AI21-Jamba-Reasoning-3B-GGUF
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View details ↗AI21-Jamba2-3B
ai21labsJamba2 3B is an ultra-compact open source model designed to bring enterprise-grade reliability to on-device deployments. At just 3B parameters, it runs efficiently on consumer devices—iPhones, Androids, Macs, and PCs—while maintaining the grounding and instruction-following capabilities required for production use. Released under Apache 2.0 License with a 256K context window, Jamba2 3B enables developers to build reliable AI applications for edge environments. For more details, read the full release blog post. * **On-device deployment:** Runs efficiently on iPhones, Androids, Macs, and PCs * **Ultra-compact footprint:** 3B parameters enabling edge deployments with minimal resources * **Benchmark leadership:** Excels on IFBench, IFEval, Collie, and FACTS * **256K context window:** Processes long documents and knowledge bases * **Apache 2.0 License:** Fully open source for commercial use * **SSM-Transformer architecture:** Memory-efficient design for resource-constrained environments
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View details ↗AI21-Jamba2-Mini
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View details ↗AI21-Jamba2-Mini-FP8
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View details ↗Jamba-tiny-dev
ai21labsThis is a tiny Jamba model used for development, debugging and experimentation over the Jamba architecture. It has 319M parameters (instead of 52B in Jamba 1.5 Mini (and Jamba v0.1) and 398B in Jamba 1.5 Large), and was trained on ~40B tokens. It is great for use in unittests since it is a small model (doesn't take long to download) that has valid and non-random outputs. Yet, **it did not undergo extensive training and should not be expected to generate high-quality text**.
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View details ↗Jamba-tiny-random
ai21labsThis is a tiny, dummy version of Jamba, used for debugging and experimentation over the Jamba architecture. It has 128M parameters (instead of 52B), **and is initialized with random weights and did not undergo any training.**
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View details ↗Jamba-tiny-reward-dev
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View details ↗Jamba-v0.1
ai21labsThis is the base version of the Jamba model. We’ve since released a better, instruct-tuned version, Jamba-1.5-Mini. For even greater performance, check out the scaled-up Jamba-1.5-Large. Jamba is a state-of-the-art, hybrid SSM-Transformer LLM. It delivers throughput gains over traditional Transformer-based models, while outperforming or matching the leading models of its size class on most common benchmarks. Jamba is the first production-scale Mamba implementation, which opens up interesting research and application opportunities. While this initial experimentation shows encouraging gains, we expect these to be further enhanced with future optimizations and explorations.
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