Instructions to use kevinpro/MistralMathOctopus-MAPO-DPO-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kevinpro/MistralMathOctopus-MAPO-DPO-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kevinpro/MistralMathOctopus-MAPO-DPO-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kevinpro/MistralMathOctopus-MAPO-DPO-7B")
model = AutoModelForCausalLM.from_pretrained("kevinpro/MistralMathOctopus-MAPO-DPO-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kevinpro/MistralMathOctopus-MAPO-DPO-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kevinpro/MistralMathOctopus-MAPO-DPO-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kevinpro/MistralMathOctopus-MAPO-DPO-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kevinpro/MistralMathOctopus-MAPO-DPO-7B

SGLang

How to use kevinpro/MistralMathOctopus-MAPO-DPO-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "kevinpro/MistralMathOctopus-MAPO-DPO-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kevinpro/MistralMathOctopus-MAPO-DPO-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "kevinpro/MistralMathOctopus-MAPO-DPO-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kevinpro/MistralMathOctopus-MAPO-DPO-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kevinpro/MistralMathOctopus-MAPO-DPO-7B with Docker Model Runner:
```
docker model run hf.co/kevinpro/MistralMathOctopus-MAPO-DPO-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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MAPO: Advancing Multilingual Reasoning through Multilingual Alignment-as-Preference Optimization

🔥Our paper

https://arxiv.org/abs/2401.06838

🔥Github Project

https://github.com/NJUNLP/MAPO

🔥Open Multilingual Reasoning Leaderboard

https://huggingface.co/spaces/kevinpro/Open-Multilingual-Reasoning-Leaderboard

Benchmarks

System	MSVAMP	MGSM	MNumGLUESub
GPT-3.5-Turbo	46.6	42.2	49.4
MAmmoTH 7B	26.3	21.3	24.2
WizardMath 7B	32.5	23.0	28.7
MetaMath 7B	46.2	37.0	43.2
QAlign 7B	57.2	49.6	-
MathOctopus 7B	41.2	39.5	37.1
+ MAPO-DPO(ours)🔥	57.4	41.6	50.4
MetaMathOctopus 7B	53.0	45.5	39.2
+ MAPO-DPO(ours) 👑	64.7	51.6	52.9
MistralMathOctopus 7B	59.0	58.0	56.8
+ MAPO-DPO(ours) 👑	74.6	67.3	70.0

System	MSVAMP	MGSM	MNumGLUESub
GPT-3.5-Turbo	46.6	42.2	49.4
MAmmoTH 13B	38.6	28.9	29.5
WizardMath 13B	35.7	28.3	29.0
MetaMath 13B	46.2	43.9	43.3
QAlign 13B	62.6	57.1	-
MathOctopus 13B	51.8	46.0	40.3
+ MAPO-DPO(ours)🔥	60.1	48.5	53.8
MetaMathOctopus 13B	56.3	51.4	49.5
+ MAPO-DPO(ours) 👑	67.0	58.0	59.8

Citation

If you find this model helpful, feel free to cite our paper:

@misc{she2024mapo,
      title={MAPO: Advancing Multilingual Reasoning through Multilingual Alignment-as-Preference Optimization}, 
      author={Shuaijie She and Wei Zou and Shujian Huang and Wenhao Zhu and Xiang Liu and Xiang Geng and Jiajun Chen},
      year={2024},
      eprint={2401.06838},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}