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+---
+license: apache-2.0
+language:
+- zh
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+<div align="center">
+<img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img> 
+</div>
+
+<p align="center">
+<a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> |
+<a href="https://github.com/OpenBMB/MiniCPM/blob/main/docs/MiniCPM_SALA.pdf" target="_blank">Technical Report</a> |
+<a href="https://mp.weixin.qq.com/s/KIhH2nCURBXuFXAtYRpuXg?poc_token=HBIsUWijxino8oJ5s6HcjcfXFRi0Xj2LJlxPYD9c">Join Us</a>
+</p>
+<p align="center">
+👋 Contact us in <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a>
+</p>
+
+## What's New
+- [2026.02.11] **[MiniCPM-SALA](https://huggingface.co/openbmb/MiniCPM-SALA)** is released! This is the first large-scale hybrid model effectively integrating sparse and linear attention for million-token context modeling. You can find technical report [here](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf).🔥🔥🔥
+
+### Highlights
+
+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 pure linear models.
+
+## Introduction
+
+MiniCPM-SALA is an efficient hybrid model in which 25% of the layers adopt [InfLLM-V2](https://arxiv.org/abs/2509.24663) and the remaining 75% utilize Lightning Attention. This architecture enables inference of one million tokens on consumer GPUs such as the NVIDIA RTX 5090.
+
+- **SALA Hybrid Attention Mechanism**
+  - Integrates 25% InfLLM-V2 and 75% Lightning Attention, effectively leveraging the granular focus of sparse attention for local details and the high efficiency of linear attention for broad context.
+
+- **Transformer-to-Hybrid Continue Training**
+  - Circumvents the inefficiencies of cold-start training by performing an architectural transformation on the pre-trained weights, thereby reducing the total training budget to approximately 25% relative to training a comparable model from scratch.
+
+- **[HyPE](https://arxiv.org/abs/2601.22156) (Hybrid Positional Encoding)**
+  - Harmonizes the performance across both short and long contexts, which can maintain general capabilities (e.g., knowledge, mathematics, and coding) comparable to modern full-attention models like Qwen3-8B and achieve substantial advantages across multiple long-context benchmarks.
+
+- **Efficient Inference on Long Sequences**
+  - Achieves up to 3.5x the inference speed of Qwen3-8B at a sequence length of 256K tokens on A6000D, supports inference at context lengths of up to 1M tokens on both NVIDIA A6000D and 5090 GPUs, whereas Qwen3-8B fails at this length due to out-of-memory (OOM) errors.
+
+## Usage
+
+### HuggingFace
+
+Our model is readily compatible with 🤗 Hugging Face transformers. You can perform inference with our model as follows:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = "openbmb/MiniCPM-SALA"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, device_map="auto")
+model.eval()
+
+prompts = ["My name is", "The capital of China is"]
+with torch.no_grad():
+    inputs = tokenizer(prompts, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs)
+output_texts = tokenizer.batch_decode(outputs)
+print(output_texts)
+```
+
+### SGLang
+
+#### Requirements
+
+- CUDA 12.x or higher
+- `gcc` / `g++` compiler
+- `uv` package manager (script will check)
+
+#### Installation
+
+```bash
+# Clone repository
+git clone -b minicpm_sala https://github.com/OpenBMB/sglang.git
+cd sglang
+
+# One-click installation (creates venv and compiles all dependencies)
+bash install_minicpm_sala.sh
+
+# Or specify PyPI mirror
+bash install_minicpm_sala.sh https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
+```
+
+The installation script performs the following steps:
+
+1. Creates `sglang_minicpm_sala_env` virtual environment (Python 3.12)
+2. Clones dependencies to `3rdparty/` (infllmv2) and initializes submodules (sparse_kernel)
+3. Installs MiniCPM-SALA (current repo)
+4. Compiles and installs `infllmv2_cuda_impl`
+5. Compiles and installs `sparse_kernel`
+6. Installs `tilelang` & `flash-linear-attention`
+
+#### Usage
+
+```bash
+# Activate environment
+source sglang_minicpm_sala_env/bin/activate
+
+# Launch Inference Server (Replace MODEL_PATH with actual path)
+MODEL_PATH=/path/to/your/MiniCPM-SALA
+
+python3 -m sglang.launch_server \
+    --model ${MODEL_PATH} \
+    --trust-remote-code \
+    --disable-radix-cache \
+    --attention-backend minicpm_flashinfer \
+    --chunked-prefill-size 8192 \
+    --max-running-requests 32 \
+    --skip-server-warmup \
+    --port 31111 \
+    --dense-as-sparse
+```
+
+| Parameter | Description |
+|-----------|-------------|
+| `--trust-remote-code` | Allow custom code in model |
+| `--disable-radix-cache` | Disable RadixAttention prefix cache |
+| `--attention-backend minicpm_flashinfer` | Use MiniCPM FlashInfer backend |
+| `--chunked-prefill-size 8192` | Chunked prefill size |
+| `--max-running-requests 32` | Max concurrent requests |
+| `--skip-server-warmup` | Skip server warmup |
+| `--port 31111` | Server port |
+| `--dense-as-sparse` | Use dense-as-sparse mode |
+
+#### Manual Installation
+
+If the script doesn't work for you, follow these steps:
+
+```bash
+# 0. Ensure uv is installed
+pip install uv
+
+# 1. Create venv
+uv venv --python 3.12 sglang_minicpm_sala_env
+source sglang_minicpm_sala_env/bin/activate
+
+# 2. Install SGLang
+uv pip install --upgrade pip setuptools wheel
+uv pip install -e ./python[all]
+
+# 3. Compile CUDA Extensions
+# (Ensure dependencies are cloned to 3rdparty/)
+cd 3rdparty/infllmv2_cuda_impl && python setup.py install && cd ../..
+cd 3rdparty/sparse_kernel && python setup.py install && cd ../..
+
+# 4. Install extra deps
+uv pip install tilelang flash-linear-attention
+```
+
+#### Q&A
+
+**Q: CUDA extension compilation failed?**
+
+- Ensure CUDA 12+ is installed (`nvcc --version`).
+- Ensure `gcc` / `g++` are available.
+- If `CXX` is set to `clang++ -pthread`, manually `export CXX=g++`.
+
+
+## Evaluation Results
+
+### Efficiency Evaluation
+
+![inference_speed_a6000d](https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_sala/inference_speed_a600d.png?raw=true)
+
+![inference_speed_5090](https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_sala/inference_speed_5090.png?raw=true)
+
+### Long-Context Evaluation
+
+![long_text_evaluation](https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_sala/long_text_evaluation.png?raw=true)
+
+### Ultra-long Context Evaluation
+
+![ultra_long_text_evaluation](https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_sala/ultra_long_text_evaluation.png?raw=true)
+
+### Standard Evaluation
+
+![benchmark](https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_sala/benchmark.png?raw=true)
+
+## Statement
+- As a language model, MiniCPM-SALA generates content by learning from a vast amount of text. 
+- However, it does not possess the ability to comprehend or express personal opinions or value judgments. 
+- Any content generated by MiniCPM-SALA does not represent the viewpoints or positions of the model developers. 
+- Therefore, when using content generated by MiniCPM-SALA, users should take full responsibility for evaluating and verifying it on their own.
+
+## LICENSE
+- This repository and MiniCPM models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. 
+
+## Citation
+- Please cite our [paper](https://github.com/OpenBMB/MiniCPM/blob/main/docs/MiniCPM_SALA.pdf) if you find our work valuable.
+
+```bibtex
+@article{minicpm4,
+  title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices},
+  author={MiniCPM Team},
+  year={2025}
+}
+```