Phi-3.5-MoE-instruct
microsoft/Phi-3.5-MoE-instruct
Tracked by OpenModelStats since September 5, 2026View on Hugging Face ↗
- Downloads 30DRolling recent-window download count as reported by the source platform.
- 142.5K
- Total downloadsCumulative all-time download counter reported by the source platform.
- 2.5M
- Gained 7DIncrease in cumulative total downloads between OpenModelStats' own observations seven days apart.
- +43.7K(increase)
- 1.9%(up) vs baseline
- Gained 30DIncrease in cumulative total downloads between OpenModelStats' own observations roughly thirty days apart.
- —
- LikesCommunity likes reported by the source platform.
- 579
- +1 in 7D
- Rank · tracked modelsPosition among all eligible models tracked by OpenModelStats, ordered by recent downloads. #473 among tracked Text Generation models.
- #1,931
- 8(up 8 places)this week
- Parameters
- 41.9B
- Last source update
- Dec 10, 2025
- MomentumOpenModelStats' composite rising-model signal (0–100): a percentile blend of absolute 7-day download gains, capped percentage growth, and likes gained. See Methodology.
- 86.5
- Phi-3.5-MoE-instruct gained 43.7K downloads during the last seven tracked days.
- It moved from #477 to #473 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
Phi-3.5-MoE-instruct is a text generation model published by microsoft. OpenModelStats has tracked the model since Sep 5, 2026, most recently observing it Oct 5, 2026.
- Published
- Aug 17, 2024
- Last updated
- Dec 10, 2025
- Library
- transformers
- License
- mit
- Task
- Text Generation
- Parameters
- 41,873,153,344
- Spaces
- —
- Derivative models
- 1
- Velocity 7D/day
- 6,241
transformerssafetensorsphimoetext-generationnlpcodeconversationalcustom_codemultilingualarxiv:2404.14219arxiv:2407.13833arxiv:2403.06412
Publisher
microsoftView publisher statistics →Model family
Based on base-model relationships reported by the source's metadata.
Derivative models
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