Instructions to use gair-prox/Mistral-7B-ProXMath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gair-prox/Mistral-7B-ProXMath with Transformers:

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

pipe = pipeline("text-generation", model="gair-prox/Mistral-7B-ProXMath")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gair-prox/Mistral-7B-ProXMath")
model = AutoModelForCausalLM.from_pretrained("gair-prox/Mistral-7B-ProXMath", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gair-prox/Mistral-7B-ProXMath with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/gair-prox/Mistral-7B-ProXMath

SGLang

How to use gair-prox/Mistral-7B-ProXMath 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 "gair-prox/Mistral-7B-ProXMath" \
    --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": "gair-prox/Mistral-7B-ProXMath",
		"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 "gair-prox/Mistral-7B-ProXMath" \
        --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": "gair-prox/Mistral-7B-ProXMath",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gair-prox/Mistral-7B-ProXMath with Docker Model Runner:
```
docker model run hf.co/gair-prox/Mistral-7B-ProXMath
```

koalazf99 commited on Sep 26, 2024

Commit

9fc6069

verified ·

1 Parent(s): 9e212bf

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -2

README.md CHANGED Viewed

@@ -15,7 +15,7 @@ pipeline_tag: text-generation
   <img src="prox-teaser.png">
 </p>
-[ArXiv](http://arxiv.org/abs/xxxx) | [Data: OpenWebMath-Pro](https://huggingface.co/datasets/gair-prox/open-web-math-pro) | [Code](https://github.com/GAIR-NLP/program-every-example)
 **Mistral-7B-ProXMath** is a math-adapted Mistral-7B-v0.1 model that is continually pre-trained on [OpenWebMath-Pro](https://huggingface.co/datasets/gair-prox/open-web-math-pro) (a refined version by ProX) for **10**B tokens.
@@ -33,6 +33,10 @@ ProX models are evaluated on 9 common math reasoning benchmarks.
 ### Citation
 ```
-@misc{TBD
 }
 ```

   <img src="prox-teaser.png">
 </p>
+[ArXiv](http://arxiv.org/abs/2409.17115) | [Data: OpenWebMath-Pro](https://huggingface.co/datasets/gair-prox/open-web-math-pro) | [Code](https://github.com/GAIR-NLP/program-every-example)
 **Mistral-7B-ProXMath** is a math-adapted Mistral-7B-v0.1 model that is continually pre-trained on [OpenWebMath-Pro](https://huggingface.co/datasets/gair-prox/open-web-math-pro) (a refined version by ProX) for **10**B tokens.
 ### Citation
 ```
+@article{zhou2024programming,
+  title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale},
+  author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei},
+  journal={arXiv preprint arXiv:2409.17115},
+  year={2024}
 }
 ```