ArchiveStudio/MiniCPM-SALA
text-generation model by ArchiveStudio
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Model card & documentation
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GitHub Repo | Technical Report | Join Us 👋 Contact us in Discord and WeChat MiniCPM-SALA (Sparse Attention and Linear Attention) is the first large-scale hybrid model effectively integrating sparse and linear attention for million-token context modeling ✅ Innovative Hybrid Architecture: Synergizes 25% Sparse Attention (InfLLM-v2) for high-fidelity long context modeling with 75% Linear Attention (Lightning Attention) for global efficiency. ✅ Shattering Efficiency Walls: Breaks the "Compute Wall" and the "Memory Wall," achieving 3.5× inference speed and significantly lower KV-cache overhead compared to dense baselines. ✅ Million-Token Context: Empowered by HyPE (Hybrid Positional Embedding), it scales to 1M+ tokens while maintaining strong length generalization. ✅ HALO Adaptation: Utilizes Hybrid Attention via Layer Optimization (HALO), a novel distillation recipe that effectively transfers dense attention capabilities to the hybrid architecture, avoiding the severe performance degradation typical of…
Read the full model card ↗ · Preview checked 2026-10-30T07:13:26.796Z
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- What's New
- Highlights
- Introduction
- Inference
- HuggingFace
- SGLang
- Requirements
- Installation
- Usage
- Manual Installation
- Q&A
- Evaluation Results
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Model overview
ArchiveStudio/MiniCPM-SALA is a text-generation model repository published by ArchiveStudio on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- text-generation
- Library
- transformers
- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 9,477,203,968
- Architecture
- MiniCPMSALAForCausalLM
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-10-30T06:38:08.000Z
- Last modified
- 2026-10-30T06:38:08.000Z
Compatibility and lineage
Reported languages: zh, en
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 2026-10-30T06:49:24.903Z. Daily imports are snapshots, not real-time monitoring.
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