paradigma-inc/limite-1b-violetto
text-generation model by paradigma-inc
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Model card & documentation
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Paradigma's first model, built for high-throughput mathematical reasoning. GitHub & vLLM plugin · Release blog Limite 1B - Violetto is Paradigma’s first model, designed for high-throughput solutions of difficult mathematical problems. Limite is a 1-billion parameter dense autoregressive transformer, trained from scratch on a mixture of less than 300B highly curated tokens, capable of handling sequences up to 131k tokens long. Apart from data curation, we achieve such high sample efficiency by equipping Limite with an architecture strongly inspired by recent advancements made by the community on pre-training speedrun competitions. The base model itself scores well on math benchmarks with few-shot prompting. We leverage a mix of synthetic data generation, curated SFT and RL post-training to achieve results on competition-level math that rivals recent models tens of times larger, trained with orders of magnitudes more FLOPs. As an example, Limite achieves an average of 74.25% on BeyondAIME, with MUSE-Glimmer-30B scoring…
Read the full model card ↗ · Preview checked 2026-11-04T17:51:58.820Z
Inside the original model card — Document outline
- Limite 1B - Violetto
- Overview
- Evaluation
- Run with vLLM
- Run with Transformers
- Supported Transformers inference paths
- Prompting and intended use
- License
- Citation
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Model overview
paradigma-inc/limite-1b-violetto is a text-generation model repository published by paradigma-inc on Hugging Face. A library was not reported.
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Model facts
- Task
- text-generation
- Library
- Not reported
- Recent downloads (30 days)
- 4,542
- Cumulative likes
- 89
- Hugging Face trending score
- 52
- Reported safetensors parameters
- 1,035,253,888
- Architecture
- LimiteForCausalLM
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-09-21T18:22:11.000Z
- Last modified
- 2026-09-28T10:25:51.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-09-29T06:38:06.650Z. Daily imports are snapshots, not real-time monitoring.
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