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AxionML
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Model Details
Model Provider
AxionML
Model Tree
moonshotai/Kimi-K2.5
Input Modalities
Output Modalities
Supported Functionality
| Architecture | Mixture-of-Experts (MoE) |
| Total Parameters | 1T |
| Activated Parameters | 32B |
| Number of Layers | 61 (including 1 dense layer) |
| Number of Experts | 384 routed, 1 shared, 8 selected per token |
| Attention Mechanism | MLA |
| Activation Function | SwiGLU |
| Vision Encoder | MoonViT (400M parameters) |
| Context Length | 256K |
| Vocabulary Size | 160K |
| Benchmark | Kimi K2.5 (Thinking) |
|---|---|
| Reasoning & Knowledge | |
| HLE-Full | 30.1 |
| HLE-Full (w/ tools) | 50.2 |
| AIME 2025 | 96.1 |
| HMMT 2025 (Feb) | 95.4 |
| IMO-AnswerBench | 81.8 |
| GPQA-Diamond | 87.6 |
| MMLU-Pro | 87.1 |
| Image & Video | |
| MMMU-Pro | 78.5 |
| CharXiv (RQ) | 77.5 |
| MathVision | 84.2 |
| MathVista (mini) | 90.1 |
| ZeroBench | 9 |
| Coding | |
| SWE-bench Verified | 65.4 |
| LiveCodeBench | 74.6 |
| Codeforces | 2131 |
| Agentic | |
| TAU-Bench (Airline) | 72.6 |
| TAU-Bench (Retail) | 68.4 |
| OSWorld (15 steps) | 41.2 |
| BrowserGym | 57.3 |
Scores are from the Kimi-K2.5 model card. MXFP8 quantization is expected to produce negligible accuracy degradation (<0.5%) on these benchmarks.
This model was quantized by applying MXFP8 to the weights and activations of linear operators within transformer blocks. Vision encoder weights are kept in their original precision.
bash
python3 -m sglang.launch_server \ --model-path AxionML/Kimi-K2.5-MXFP8 \ --tp 8 \ --trust-remote-codebash
python3 examples/llm_ptq/hf_ptq.py \ --pyt_ckpt_path moonshotai/Kimi-K2.5 \ --qformat mxfp8 \ --export_path ./kimi-k2.5-mxfp8The base model was trained on data that may contain toxic language and societal biases. The quantized model inherits these limitations. It may generate inaccurate, biased, or offensive content. Please refer to the original model card for full details.
python3 -m sglang.launch_server \ --model-path AxionML/Kimi-K2.5-MXFP8 \ --tp 8 \ --trust-remote-codepython3 examples/llm_ptq/hf_ptq.py \ --pyt_ckpt_path moonshotai/Kimi-K2.5 \ --qformat mxfp8 \ --export_path ./kimi-k2.5-mxfp8