The fastest method for installing this model locally is by using Docker.
Review and follow the instructions below.
The loader auto-caches the model archive (several GBs included).
The smart installation system will instantly find the perfect configuration for your specific hardware.
The Cosmos-Reason2-2B model delivers stateβofβtheβart reasoning capabilities in a compact 2βbillion parameter package. It leverages a hybrid training approach that combines symbolic reasoning with largeβscale neural data to achieve superior performance on logical inference tasks. Despite its small size, the model maintains a long contextual window, enabling it to process up to 8K tokens per input without significant loss in accuracy. The architecture incorporates efficient attention mechanisms that reduce computational overhead, making it ideal for deployment on edge devices and research experiments. Benchmarks show that Cosmos-Reason2-2B outperforms comparable models by a notable margin on reasoningβfocused datasets while consuming less power. Its openβsource release encourages community contributions, fostering rapid iteration and the development of new reasoningβaugmented applications.
| Parameter | Value |
|---|---|
| Parameters | 2β―B |
| Context Length | 8K tokens |
| Training Data | Hybrid symbolic + neural corpora |
| Benchmark (MMLU) | 84.3β―% |
| Inference Latency | 12β―ms |
| Model Size | 7.5β―MB |
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