plamo-3-nict-2b-base
pfnet/plamo-3-nict-2b-base
Tracked by OpenModelStats since September 28, 2026View on Hugging Face ↗
- Downloads 30DRolling recent-window download count as reported by the source platform.
- 2,724
- Total downloadsCumulative all-time download counter reported by the source platform.
- 169.2K
- Gained 7DIncrease in cumulative total downloads between OpenModelStats' own observations seven days apart.
- +771(increase)
- 0.5%(up) vs baseline
- Gained 30DIncrease in cumulative total downloads between OpenModelStats' own observations roughly thirty days apart.
- —
- LikesCommunity likes reported by the source platform.
- 6
- +0 in 7D
- Rank · tracked modelsPosition among all eligible models tracked by OpenModelStats, ordered by recent downloads. #4404 among tracked Text Generation models.
- #16,747
- 185(down 185 places)this week
- Parameters
- 2.6B
- Last source update
- Aug 21, 2026
- MomentumOpenModelStats' composite rising-model signal (0–100): a percentile blend of absolute 7-day download gains, capped percentage growth, and likes gained. See Methodology.
- 49.2
- plamo-3-nict-2b-base gained 771 downloads during the last seven tracked days.
- It moved from #4333 to #4404 among tracked Text Generation models this week.
Downloads over time
Cumulative total downloads observed by OpenModelStatsLikes over time
Rank among tracked models
Lower is betterOverview
plamo-3-nict-2b-base is a text generation model published by pfnet. OpenModelStats has tracked the model since Sep 28, 2026, most recently observing it Oct 5, 2026.
- Published
- Oct 24, 2025
- Last updated
- Aug 21, 2026
- Library
- transformers
- License
- other
- Task
- Text Generation
- Parameters
- 2,603,344,384
- Spaces
- —
- Derivative models
- —
- Velocity 7D/day
- 110
transformerssafetensorsplamo3text-generationconversationalcustom_codeenjadataset:HuggingFaceFW/fineweb-2dataset:HuggingFaceTB/smollm-corpusdataset:bigcode/the-stack-v2-train-smol-idsdataset:nvidia/Nemotron-Pretraining-Dataset-sample
Publisher
pfnetView publisher statistics →Similar models
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