chenchenghao/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 12 Packages Projects Releases Wiki Activity

[AMD] [MiniMax-M2.5 Day 0] Add MiniMax-M2.5 nightly accuracy test (#19443)

Browse Source
This commit is contained in:
Michael
2026-02-27 02:39:33 -08:00
committed by GitHub
parent f69ca93d49
commit 403195d59d
6 changed files with 653 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

25
docs/basic_usage/minimax_m2.md
View File

@@ -1,13 +1,14 @@
# MiniMax M2.1/M2 Usage
# MiniMax M2.5/M2.1/M2 Usage
[MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1) and [MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) are advanced large language models created by [MiniMax](https://www.minimax.io/).
[MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5), [MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1), and [MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) are advanced large language models created by [MiniMax](https://www.minimax.io/).
MiniMax-M2 series redefines efficiency for agents. It's a compact, fast, and cost-effective MoE model (230 billion total parameters with 10 billion active parameters) built for elite performance in coding and agentic tasks, all while maintaining powerful general intelligence. With just 10 billion activated parameters, MiniMax-M2 provides the sophisticated, end-to-end tool use performance expected from today's leading models, but in a streamlined form factor that makes deployment and scaling easier than ever.
The MiniMax-M2 series redefines efficiency for agents. These compact, fast, and cost-effective MoE models (230 billion total parameters with 10 billion active parameters) are built for elite performance in coding and agentic tasks, all while maintaining powerful general intelligence. With just 10 billion activated parameters, the MiniMax-M2 series provides sophisticated, end-to-end tool use performance expected from today's leading models, but in a streamlined form factor that makes deployment and scaling easier than ever.
## Supported Models
This guide applies to the following models. You only need to update the model name during deployment. The following examples use **MiniMax-M2**:
- [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5)
- [MiniMaxAI/MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1)
- [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2)
@@ -49,6 +50,24 @@ python -m sglang.launch_server \
--mem-fraction-static 0.85
```
### AMD GPUs (MI300X/MI325X/MI355X)
8-GPU deployment command:
```bash
SGLANG_USE_AITER=1 python -m sglang.launch_server \
--model-path MiniMaxAI/MiniMax-M2.5 \
--tp-size 8 \
--ep-size 8 \
--attention-backend aiter \
--tool-call-parser minimax-m2 \
--reasoning-parser minimax-append-think \
--host 0.0.0.0 \
--trust-remote-code \
--port 8000 \
--mem-fraction-static 0.85
```
## Testing Deployment
After startup, you can test the SGLang OpenAI-compatible API with the following command: