SLAI-AITP/SLAI-T-Rex-Flash

SLAI T-Rex-Flash (DeepSeek-V4-Flash-OR)

SLAI T-Rex-Flash is an Operations Research (OR)–specialized model built from DeepSeek-V4-Flash through full-parameter continued pre-training (CPT) followed by supervised fine-tuning (SFT). It is designed for solver-oriented mathematical modeling, including formulation, Gurobi program generation, feasibility-oriented tasks, and structure-sensitive optimization modeling.

• 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-Flash
Base model	DeepSeek-V4-Flash
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

The CPT stage supplies OR terminology, formulation patterns, solver APIs, and structural priors. The SFT stage aligns these capabilities with task instructions, output contracts, mathematical explanations, and executable Gurobi-style implementations. The reported SFT recipe uses an 8,192-token sequence length, AdamW, a global batch size of 128, and full-parameter optimization.

Download

Install ModelScope and download the checkpoint:

python3 -m pip install -U modelscope

from modelscope import snapshot_download

model_dir = snapshot_download("SLAIAITP/DeepSeek-V4-Flash-OR")
print(model_dir)

The checkpoint should be served with a runtime that supports the DeepSeek-V4 architecture and the checkpoint format in this repository. Distributed serving flags depend on the target hardware, runtime version, and available memory. Check the repository configuration files before deployment.

Intended Use

SLAI T-Rex-Flash is intended for research and development involving:

• translating natural-language OR problems into mathematical programs;
• identifying variables, objectives, and constraints;
• generating Gurobi-style Python implementations;
• producing solver-compatible models under strict output contracts;
• studying domain-adaptive CPT and SFT for structured reasoning.

A typical prompt should state the optimization problem, all required data, the expected solver behavior, and the output contract. For tagged modeling tasks, the training data commonly use <think>, <model>, and <python> sections. Code-only and LP-writing tasks use their task-specific contracts instead.

Evaluation

The following results are reported in the technical report under zero-shot Pass@1 evaluation. Overall is the unweighted mean of the four OR benchmarks.

Model	NL4OPT	OptiBench	B4O-Feasible	B4O-ORGEval	Overall
DeepSeek-V4-Flash	84.08	63.33	60.47	34.26	60.54
SLAI T-Rex-Flash	89.52	67.12	71.22	59.39	71.81

These results correspond to the complete CPT+SFT pipeline. The reported overall score improves by 11.27 percentage points over the original DeepSeek-V4-Flash checkpoint. The largest absolute gain is on B4O-ORGEval, which evaluates structural equivalence rather than execution success alone.

The report also evaluates general-capability retention:

Benchmark	SLAI T-Rex-Flash	Base
MMLU, 5-shot	88.5	87.6
MMLU-Pro, 5-shot	69.2	71.0
CMMLU, 5-shot	92.4	92.1
HumanEval, 0-shot	68.8	69.4
GSM8K, 8-shot	90.3	89.8
MATH, 4-shot	56.7	58.4

Evaluation numbers should be compared only under the same prompt templates, 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}
}