SLAI-AITP/SLAI-T-Rex-Pro

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.

• Paper: SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD
• Paper PDF: arXiv:2607.20145
• Code and training recipes: SLAI-AITP/SLAI-T-Rex

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:

@misc{li2026slaitrexfullparameterposttraining,
  title        = {SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD},
  author       = {Dongfang Li and others},
  year         = {2026},
  eprint       = {2607.20145},
  archivePrefix= {arXiv},
  url          = {https://arxiv.org/abs/2607.20145}
}