Instructions to use microsoft/rho-math-7b-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/rho-math-7b-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/rho-math-7b-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/rho-math-7b-v0.1")
model = AutoModelForCausalLM.from_pretrained("microsoft/rho-math-7b-v0.1")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use microsoft/rho-math-7b-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/rho-math-7b-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/rho-math-7b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/microsoft/rho-math-7b-v0.1

SGLang

How to use microsoft/rho-math-7b-v0.1 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 "microsoft/rho-math-7b-v0.1" \
    --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": "microsoft/rho-math-7b-v0.1",
		"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 "microsoft/rho-math-7b-v0.1" \
        --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": "microsoft/rho-math-7b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use microsoft/rho-math-7b-v0.1 with Docker Model Runner:
```
docker model run hf.co/microsoft/rho-math-7b-v0.1
```

zhibin-msft commited on Apr 18, 2024

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@@ -18,9 +18,7 @@ Rho-1: Not All Tokens Are What You Need
   <a href="https://arxiv.org/abs/2404.07965"><b>[📜 Arxiv]</b></a> •
   <a href="https://huggingface.co/papers/2404.07965"><b>[💬 HF Paper]</b></a> •
   <a href="https://huggingface.co/microsoft/rho-math-1b-v0.1"><b>[🤗 Models]</b></a> •
-  <a href="https://github.com/microsoft/rho"><b>[🐱 GitHub]</b></a> •
-  <a href="https://twitter.com/zebgou/status/1778676535404396697"><b>[🐦 Twitter]</b></a> •
-  <a href="https://huggingface.co/spaces/zubingou/rho-1"><b>[🤖 Gradio Demo]</b></a>
 </p>
 <p align="center">
@@ -32,7 +30,6 @@ Rho-1: Not All Tokens Are What You Need
 ## 🔥 News
-- [2024/04/14] 🚀🚀🚀 We release [Gradio demo of Rho-1 Code Interpreter](https://huggingface.co/spaces/zubingou/rho-1), try it out!
 - [2024/04/12] 🔥🔥🔥 Rho-Math-v0.1 models released at 🤗 HuggingFace!
     - [Rho-Math-1B](https://huggingface.co/microsoft/rho-math-1b-v0.1) and [Rho-Math-7B](https://huggingface.co/microsoft/rho-math-7b-v0.1) achieve 15.6% and 31.0% few-shot accuracy on MATH dataset, respectively — matching DeepSeekMath with only 3\% of the pretraining tokens.
     - [Rho-Math-1B-Interpreter](https://huggingface.co/microsoft/rho-math-1b-interpreter-v0.1) is the first 1B LLM that achieves over 40% accuracy on MATH.

   <a href="https://arxiv.org/abs/2404.07965"><b>[📜 Arxiv]</b></a> •
   <a href="https://huggingface.co/papers/2404.07965"><b>[💬 HF Paper]</b></a> •
   <a href="https://huggingface.co/microsoft/rho-math-1b-v0.1"><b>[🤗 Models]</b></a> •
+  <a href="https://github.com/microsoft/rho"><b>[🐱 GitHub]</b></a>
 </p>
 <p align="center">
 ## 🔥 News
 - [2024/04/12] 🔥🔥🔥 Rho-Math-v0.1 models released at 🤗 HuggingFace!
     - [Rho-Math-1B](https://huggingface.co/microsoft/rho-math-1b-v0.1) and [Rho-Math-7B](https://huggingface.co/microsoft/rho-math-7b-v0.1) achieve 15.6% and 31.0% few-shot accuracy on MATH dataset, respectively — matching DeepSeekMath with only 3\% of the pretraining tokens.
     - [Rho-Math-1B-Interpreter](https://huggingface.co/microsoft/rho-math-1b-interpreter-v0.1) is the first 1B LLM that achieves over 40% accuracy on MATH.