---
license: apache-2.0
language:
- en
base_model:
- deepseek-ai/DeepSeek-V4-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](https://arxiv.org/abs/2607.20145)
- Paper PDF: [arXiv:2607.20145](https://arxiv.org/pdf/2607.20145)
- Code and training recipes: [SLAI-AITP/SLAI-T-Rex](https://github.com/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:

```bash
python3 -m pip install -U modelscope
```

```python
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:

```bibtex
@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}
}
```