xuebi
commited on
Commit
·
1ccbe32
1
Parent(s):
8d70a8a
update
Browse files
README.md
CHANGED
|
@@ -109,7 +109,7 @@ Furthermore, across specific benchmarks—including test case generation, code p
|
|
| 109 |
| SWE-Review | 8.9 | 3.4 | 10.5 | 16.2 | x | x | 6.4 |
|
| 110 |
| OctoCodingbench | 26.1 | 13.3 | 22.8 | 36.2 | 22.9 | x | 26.0 |
|
| 111 |
|
| 112 |
-
To evaluate the model's full-stack capability to architect complete, functional applications "from zero to one," we established a novel benchmark: VIBE (Visual & Interactive Benchmark for Execution). This suite encompasses five core subsets: Web, Simulation, Android, iOS, and Backend. Distinguishing itself from traditional benchmarks, VIBE leverages an innovative Agent-as-a-Verifier (AaaV) paradigm to automatically assess the interactive logic and visual aesthetics of generated applications within a real runtime environment.
|
| 113 |
|
| 114 |
MiniMax-M2.1 delivers outstanding performance on the VIBE aggregate benchmark, achieving an average score of 88.6—demonstrating robust full-stack development capabilities. It excels particularly in the VIBE-Web (91.5) and VIBE-Android (89.7) subsets.
|
| 115 |
|
|
@@ -169,6 +169,10 @@ We recommend using [vLLM](https://github.com/vllm-project/vllm) to serve MiniMax
|
|
| 169 |
|
| 170 |
We recommend using [Transformers](https://github.com/huggingface/transformers) to serve MiniMax-M2.1. Please refer to our [Transformers Deployment Guide](./docs/transformers_deploy_guide.md).
|
| 171 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 172 |
### Inference Parameters
|
| 173 |
|
| 174 |
We recommend using the following parameters for best performance: `temperature=1.0`, `top_p = 0.95`, `top_k = 40`. Default system prompt:
|
|
|
|
| 109 |
| SWE-Review | 8.9 | 3.4 | 10.5 | 16.2 | x | x | 6.4 |
|
| 110 |
| OctoCodingbench | 26.1 | 13.3 | 22.8 | 36.2 | 22.9 | x | 26.0 |
|
| 111 |
|
| 112 |
+
To evaluate the model's full-stack capability to architect complete, functional applications "from zero to one," we established a novel benchmark: [VIBE (Visual & Interactive Benchmark for Execution)](https://huggingface.co/datasets/MiniMaxAI/VIBE). This suite encompasses five core subsets: Web, Simulation, Android, iOS, and Backend. Distinguishing itself from traditional benchmarks, VIBE leverages an innovative Agent-as-a-Verifier (AaaV) paradigm to automatically assess the interactive logic and visual aesthetics of generated applications within a real runtime environment.
|
| 113 |
|
| 114 |
MiniMax-M2.1 delivers outstanding performance on the VIBE aggregate benchmark, achieving an average score of 88.6—demonstrating robust full-stack development capabilities. It excels particularly in the VIBE-Web (91.5) and VIBE-Android (89.7) subsets.
|
| 115 |
|
|
|
|
| 169 |
|
| 170 |
We recommend using [Transformers](https://github.com/huggingface/transformers) to serve MiniMax-M2.1. Please refer to our [Transformers Deployment Guide](./docs/transformers_deploy_guide.md).
|
| 171 |
|
| 172 |
+
### Other Inference Engines
|
| 173 |
+
|
| 174 |
+
- [KTransformers](https://github.com/kvcache-ai/ktransformers)
|
| 175 |
+
|
| 176 |
### Inference Parameters
|
| 177 |
|
| 178 |
We recommend using the following parameters for best performance: `temperature=1.0`, `top_p = 0.95`, `top_k = 40`. Default system prompt:
|