Instructions to use Mathoctopus/Parallel_xRFT_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mathoctopus/Parallel_xRFT_7B with Transformers:

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

pipe = pipeline("text-generation", model="Mathoctopus/Parallel_xRFT_7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mathoctopus/Parallel_xRFT_7B")
model = AutoModelForCausalLM.from_pretrained("Mathoctopus/Parallel_xRFT_7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Mathoctopus/Parallel_xRFT_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mathoctopus/Parallel_xRFT_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mathoctopus/Parallel_xRFT_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mathoctopus/Parallel_xRFT_7B

SGLang

How to use Mathoctopus/Parallel_xRFT_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 "Mathoctopus/Parallel_xRFT_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": "Mathoctopus/Parallel_xRFT_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 "Mathoctopus/Parallel_xRFT_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": "Mathoctopus/Parallel_xRFT_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mathoctopus/Parallel_xRFT_7B with Docker Model Runner:
```
docker model run hf.co/Mathoctopus/Parallel_xRFT_7B
```

compasszzn commited on Oct 18, 2023

Commit

25889d8

1 Parent(s): df603ea

Update README.md

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@@ -116,4 +116,4 @@ Or you can directly download them from
 | MathOctupos<sup>P</sup>-33B     | 56.0    | 52.5    |
 | MathOctupos<sup>C</sup>-33B     | 53.7    | 51.5    |
 ## Intended Uses
-These models are trained for research purposes. They are designed to solve multilingual math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed.

 | MathOctupos<sup>P</sup>-33B     | 56.0    | 52.5    |
 | MathOctupos<sup>C</sup>-33B     | 53.7    | 51.5    |
 ## Intended Uses
+These models are trained for research purposes. They are designed to solve multilingual math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed.