SLAI T-Rex-Pro
SLAI T-Rex-Pro is the trillion-parameter scale-up of the SLAI T-Rex Operations Research (OR) post-training workflow. Starting from DeepSeek-V4-Pro, it applies full-parameter OR-oriented continued pre-training (CPT) followed by Clean-CoT supervised fine-tuning (SFT) on an optimized Ascend SuperPOD training stack.
Model Summary
| Item | Description |
|---|
| Model name | SLAI T-Rex-Pro |
| Base model | DeepSeek-V4-Pro |
| Domain | Operations Research and mathematical programming |
| Training | Full-parameter OR-oriented CPT followed by Clean-CoT SFT |
| Primary output | Mathematical formulation and/or solver-facing Python code |
| Training hardware | Ascend CloudMatrix384 SuperPOD with Ascend 910C NPUs |
SLAI T-Rex-Pro validates that the CPT+SFT workflow developed on DeepSeek-V4-Flash transfers to the substantially larger Pro model. The system-side optimizations reported for DeepSeek-V4-Pro increase Model FLOPs Utilization from 11.67% to 34.22%, a 2.93× improvement over the open-source baseline recipe. This is a training-system result rather than an inference-speed claim.
Download and Deployment
Download the files from this repository using the repository platform's standard snapshot or Git LFS workflow. Because this is a very large MoE checkpoint, verify all of the following before deployment:
- checkpoint precision and shard index integrity;
- storage capacity and host-memory requirements;
- tokenizer and DeepSeek-V4 architecture support;
- tensor, pipeline, data, and expert parallel layout;
- runtime support for the checkpoint's attention and MoE operators.
The public code repository documents the checkpoint-preparation and training-side conversion boundary. Serving commands are intentionally hardware- and runtime-specific; use a DeepSeek-V4-compatible serving stack and validate a small deterministic request before scaling out.
Intended Use
SLAI T-Rex-Pro is intended for research and development involving:
- natural-language-to-optimization modeling;
- mathematical formulation and structural reasoning;
- Gurobi-style solver program generation;
- feasibility- and equivalence-oriented OR tasks;
- research on full-parameter post-training of very large MoE models.
Prompts should provide complete problem data, explicitly state the expected solver action, and define the required response format. Generated code and formulations must be independently checked before use.
Evaluation
The following scale-up results are reported under zero-shot Pass@4 evaluation. Overall is the unweighted mean of the four OR benchmarks.
| Model | NL4OPT | OptiBench | B4O-Feasible | B4O-ORGEval | Overall | Gain vs. Base |
|---|
| DeepSeek-V4-Pro | 86.51 | 70.00 | 80.23 | 43.91 | 70.16 | — |
| DeepSeek-V4-Pro + SFT | 89.97 | 69.50 | 86.04 | 61.43 | 76.74 | +6.58 |
| SLAI T-Rex-Pro | 92.04 | 70.17 | 85.17 | 61.93 | 77.33 | +7.17 |
The complete pipeline obtains the strongest aggregate OR result. It does not dominate SFT-only on every benchmark: SFT-only scores 86.04 on B4O-Feasible, compared with 85.17 for SLAI T-Rex-Pro. The largest improvement over the base checkpoint is on B4O-ORGEval.
The report also provides a complementary general-capability check:
| Model | AIME 2024 | AIME 2025 | CMMLU | HumanEval | LiveCodeBench |
|---|
| DeepSeek-V4-Pro | 83.33 | 70.00 | 93.88 | 100.00 | 55.00 |
| DeepSeek-V4-Pro + SFT | 83.33 | 76.67 | 93.75 | 100.00 | 72.00 |
| SLAI T-Rex-Pro | 90.00 | 86.67 | 93.12 | 100.00 | 67.00 |
Evaluation numbers should be compared only under the same prompts, decoding budgets, benchmark versions, solver environment, and scoring implementation described in the paper.
Citation
If you use this model, please cite the technical report:
1@misc{li2026slaitrexfullparameterposttraining,
2 title = {SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD},
3 author = {Dongfang Li and others},
4 year = {2026},
5 eprint = {2607.20145},
6 archivePrefix= {arXiv},
7 url = {https://arxiv.org/abs/2607.20145}
8}
