THRIFT — Targeted Reduction for Inference and Fine-Tuning

A performance-optimized variant of the base model that delivers faster responses and lower memory usage while preserving quality for everyday tasks, developed by VibeStud.io.

TLDR

We, over-caffinated researchers at VibeStud.io wanted to create a 50% pruned version of the SOTA MiniMax M2 that is best suited for local/air-gapped coding. This version we achieved ~25%. A 50% pruned version is under development while a not so sucky team of ours is working on a 50% pruned version of Kimi K2 Thinking.We’re writing the paper and expanding the evaluation set to substantiate the results. Check back later, cheers!

Why it’s useful

Lower latency: Snappier responses for interactive apps and chatbots.
Smaller memory footprint: Runs on cheaper GPUs or with fewer resources per replica.
Higher throughput: Serve more concurrent users at the same cost.
Deployment-friendly: Drop-in replacement for the base model in most inference stacks.
Adaptable: Supports light fine-tuning to match your domain and style guidelines.

Intended use

General chat and coding assistance
Enterprise assistants with strict latency/VRAM budgets
Batch or realtime serving in cloud and on-prem environments
Edge or cost-sensitive deployments where efficiency matters

When to use it

You’re constrained by GPU memory or need shorter response times
You want to increase QPS without scaling infrastructure
You need a model that is “good enough” for most tasks at a better cost profile

Model Comparison Report

Models Under Evaluation

Model	Type
ModelCloud/MiniMax-M2-BF16	Base Model
VibeStudio/MiniMax-M2-THRIFT	Compressed/Optimized

Evaluation Dates: November 7–9, 2025

📊 Results Comparison

1) Multiple Choice Q&A (lm-eval)

Overall MMLU Performance

Model	MMLU Overall	Humanities	STEM	Social Sciences	Other
MiniMax-M2-BF16	83.16%	77.45%	80.91%	90.02%	87.29%
MiniMax-M2-THRIFT	77.72%	70.14%	77.61%	86.84%	80.27%
Δ (Difference)	-5.44%	-7.31%	-3.30%	-3.18%	-7.02%

Individual Task Performance

Task	BF16 (Base)	THRIFT-BF16	Difference
arc_challenge (acc_norm)	73.21%	61.01%	-12.20% ⬇️
arc_easy	88.30%	83.08%	-5.22% ⬇️
boolq	87.95%	84.95%	-3.00% ⬇️
hellaswag (acc_norm)	83.00%	77.09%	-5.91% ⬇️
mmlu	83.16%	77.72%	-5.44% ⬇️
openbookqa (acc_norm)	48.60%	43.00%	-5.60% ⬇️
rte	75.45%	80.14%	+4.69% ⬆️
winogrande	76.48%	74.90%	-1.58% ⬇️

Average Accuracy Drop: -4.28%

2) Code Generation (EvalPlus)

MBPP Results (Python, 378 problems)

Model	MBPP (base)	MBPP+ (extended)	Average
MiniMax-M2-BF16	73.8%	64.0%	68.9%
MiniMax-M2-THRIFT	70.1%	60.1%	65.1%
Δ (Difference)	-3.7%	-3.9%	-3.8%

HumanEval Results (164 problems)

Model	HumanEval (base)	HumanEval+ (extended)	Average
MiniMax-M2-BF16	72.6%	71.3%	72.0%
MiniMax-M2-THRIFT	65.2%	63.4%	64.3%
Δ (Difference)	-7.4%	-7.9%	-7.7%

3) Math Benchmarks

GSM8K Results

Model	Accuracy	Problems	Status
MiniMax-M2-BF16	92.72%	1,319	✅ Complete
MiniMax-M2-THRIFT	93.25%	1,319	✅ Complete
Δ (Difference)	+0.53% ⬆️	-	THRIFT Better! ✨

Benchmark	MiniMax-M2-BF16	MiniMax-M2-THRIFT	Change
GSM8K	92.72%	93.25%	+0.53% ⬆️
MATH-500 (Levels 1-4)	91.25%	90.75%	-0.5% (near-parity)

4) LiveCodeBench (Live Coding Problems)

Model	pass@1	Problems	Status
MiniMax-M2-BF16	35.71%	182	✅ Complete
MiniMax-M2-THRIFT	36.81%	182	✅ Complete
Δ (Difference)	+1.10% ⬆️	-	THRIFT Better! ✨

📈 Analysis (Updated)

Highlights

THRIFT wins on GSM8K (+0.53%) and LiveCodeBench (+1.10%), and on RTE (+4.69%).
BF16 leads on broad MMLU, HumanEval, MBPP, and tasks like arc_challenge.

Compression Trade-off

Average knowledge-task drop for THRIFT is ~4–5%, with math preserved or slightly improved.

Subject Breakdown (MMLU)

Category	BF16 (Base)	THRIFT-BF16	Difference	Status
High School Government	97.93%	94.82%	-3.11%	✅ Still Excellent
High School Psychology	95.41%	93.58%	-1.83%	✅ Well Preserved
Marketing	95.73%	91.88%	-3.85%	✅ Good
Professional Medicine	92.28%	79.78%	-12.50%	⚠️ Notable Drop
Clinical Knowledge	92.83%	85.66%	-7.17%	⚠️ Moderate Drop

sglang Deployment with Python

It is recommended to use a virtual environment (such as venv, conda, or uv) to avoid dependency conflicts.

We recommend installing SGLang in a fresh Python environment:

git clone -b v0.5.4.post1 https://github.com/sgl-project/sglang.git
cd sglang

# Install the python packages
pip install --upgrade pip
pip install -e "python"

Run the following command to start the SGLang server. SGLang will automatically download and cache the MiniMax-M2 model from Hugging Face.

4-GPU deployment command:

python -m sglang.launch_server \
    --model-path MiniMaxAI/MiniMax-M2 \
    --tp-size 4 \
    --tool-call-parser minimax-m2 \
    --reasoning-parser minimax-append-think \
    --host 0.0.0.0 \
    --trust-remote-code \
    --port 8000 \
    --mem-fraction-static 0.85

8-GPU deployment command:

python -m sglang.launch_server \
    --model-path MiniMaxAI/MiniMax-M2 \
    --tp-size 8 \
    --ep-size 8 \
    --tool-call-parser minimax-m2 \
    --trust-remote-code \
    --host 0.0.0.0 \
    --reasoning-parser minimax-append-think \
    --port 8000 \
    --mem-fraction-static 0.85

Testing Deployment

After startup, you can test the SGLang OpenAI-compatible API with the following command:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "MiniMaxAI/MiniMax-M2",
        "messages": [
            {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
            {"role": "user", "content": [{"type": "text", "text": "Who won the world series in 2020?"}]}
        ]
    }'

Benchmarks

See the tables above for the latest MMLU, MBPP, HumanEval, GSM8K, MATH-500, and LiveCodeBench results (updated November 9, 2025).

Research paper

Coming soon.

License

This model is derived from MiniMax-M2 and distributed under the MIT License http://github.com/MiniMax-AI/MiniMax-M2/blob/main/LICENSE

Credits

Model conversion and HF Transformers code by @Qubitum at ModelCloud.

References (BibTeX)

@article{cai2025thinking,
  title        = {Thinking with DistilQwen: A Tale of Four Distilled Reasoning and Reward Model Series},
  author       = {Cai, Wenrui and Wang, Chengyu and Yan, Junbing and Huang, Jun and Fang, Xiangzhong},
  journal      = {arXiv preprint arXiv:2511.01354},
  year         = {2025},
  eprinttype   = {arXiv},
  eprint       = {2511.01354},
  primaryclass = {cs.CL},
  institution  = {Shanghai Jiao Tong University and Alibaba Cloud Computing},
  note         = {License: arXiv.org perpetual non-exclusive license}
}

@misc{lasby-reap,
    title       = {{REAP the Experts: Why Pruning Prevails for One-Shot MoE compression}},
    author      = {Lasby, Mike and Lazarevich, Ivan and Sinnadurai, Nish and Lie, Sean and Ioannou, Yani and Thangarasa, Vithursan},
    year        = {2025},
    publisher   = {arXiv},
    note        = {arXiv:2510.13999v1 [cs]},
    url         = {https://arxiv.org/abs/2510.13999v1}, 
}

@article{yang2025wanda++,
  title        = {Wanda++: Pruning Large Language Models via Regional Gradients},
  author       = {Yang, Yifan and Zhen, Kai and Ganesh, Bhavana and Galstyan, Aram and Huybrechts, Goeric and Müller, Markus and Kübler, Jonas M. and Swaminathan, Rupak Vignesh and Mouchtaris, Athanasios and Bodapati, Sravan Babu and Susanj, Nathan and Zhang, Zheng and FitzGerald, Jack and Kumar, Abhishek},
  journal      = {arXiv preprint arXiv:2503.04992},
  year         = {2025},
  eprinttype   = {arXiv},
  eprint       = {2503.04992},
  primaryclass = {cs.CL}
}

@article{li2025tyr,
  title        = {Týr-the-Pruner: Structural Pruning LLMs via Global Sparsity Distribution Optimization},
  author       = {Li, G. and Xu, Yixing and Li, Zeping and Liu, Ji and Yin, Xuanwu and Li, Dong and Barsoum, Emad},
  journal      = {arXiv preprint arXiv:2503.09657},
  year         = {2025},
  eprinttype   = {arXiv},
  eprint       = {2503.09657},
  primaryclass = {cs.CL}
}

@article{xia2023sheared,
  title        = {Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning},
  author       = {Xia, Mengzhou and Gao, Tianyu and Zeng, Zhiyuan and Chen, Danqi},
  journal      = {arXiv preprint arXiv:2310.06694},
  year         = {2023},
  eprinttype   = {arXiv},
  eprint       = {2310.06694},
  primaryclass = {cs.CL}
}

@article{ma2023llmpruner,
  title        = {LLM-Pruner: On the Structural Pruning of Large Language Models},
  author       = {Ma, Xinyin and Fang, Gongfan and Wang, Xinchao},
  journal      = {arXiv preprint arXiv:2305.11627},
  year         = {2023},
  eprinttype   = {arXiv},
  eprint       = {2305.11627},
  primaryclass = {cs.CL}
}

@article{yang2023wanda,
  title        = {Wanda: Pruning by Weights and Activation-based Discriminant Analysis},
  author       = {Yang, Yifan and Ganesh, Bhavana and Galstyan, Aram and Huybrechts, Goeric and Müller, Markus and Kübler, Jonas M. and Swaminathan, Rupak Vignesh and Mouchtaris, Athanasios and Bodapati, Sravan Babu and Zhang, Zheng and FitzGerald, Jack and Kumar, Abhishek},
  journal      = {arXiv preprint arXiv:2306.11695},
  year         = {2023},
  eprinttype   = {arXiv},
  eprint       = {2306.11695},
  primaryclass = {cs.CL}
}

@article{frantar2023sparsegpt,
  title        = {SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot},
  author       = {Frantar, Elias and Alistarh, Dan},
  journal      = {arXiv preprint arXiv:2301.00774},
  year         = {2023},
  eprinttype   = {arXiv},
  eprint       = {2301.00774},
  primaryclass = {cs.CL}
}

@article{dettmers2023qlora,
  title        = {QLoRA: Efficient Finetuning of Quantized LLMs},
  author       = {Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
  journal      = {arXiv preprint arXiv:2307.02973},
  year         = {2023},
  eprinttype   = {arXiv},
  eprint       = {2307.02973},
  primaryclass = {cs.CL}
}