Instructions to use MathGenie/MathGenie-InterLM-20B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MathGenie/MathGenie-InterLM-20B with Transformers:

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

pipe = pipeline("text-generation", model="MathGenie/MathGenie-InterLM-20B", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MathGenie/MathGenie-InterLM-20B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MathGenie/MathGenie-InterLM-20B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MathGenie/MathGenie-InterLM-20B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MathGenie/MathGenie-InterLM-20B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MathGenie/MathGenie-InterLM-20B

SGLang

How to use MathGenie/MathGenie-InterLM-20B 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 "MathGenie/MathGenie-InterLM-20B" \
    --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": "MathGenie/MathGenie-InterLM-20B",
		"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 "MathGenie/MathGenie-InterLM-20B" \
        --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": "MathGenie/MathGenie-InterLM-20B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MathGenie/MathGenie-InterLM-20B with Docker Model Runner:
```
docker model run hf.co/MathGenie/MathGenie-InterLM-20B
```

MathGenie commited on Apr 20, 2024

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@@ -35,10 +35,12 @@ You can refer to the [project homepage](https://mathgenie.github.io/) and [the p
 ### Models
 Our [MathGenie-InterLM-20B](https://huggingface.co/MathGenie/MathGenie-InterLM-20B) model is available at Huggingface now.
 | Base Model   | Model                                                        |
 | ------------ | ------------------------------------------------------------ |
 | InternLM-20B | [MathGenie-InterLM-20B](https://huggingface.co/MathGenie/MathGenie-InterLM-20B) |
 ### Inference & Evaluation

 ### Models
 Our [MathGenie-InterLM-20B](https://huggingface.co/MathGenie/MathGenie-InterLM-20B) model is available at Huggingface now.
+Our [MathGenie-Mixtral-8x7B](https://huggingface.co/MathGenie/MathGenie-Mixtral-8x7B) model is available at Huggingface now.
 | Base Model   | Model                                                        |
 | ------------ | ------------------------------------------------------------ |
 | InternLM-20B | [MathGenie-InterLM-20B](https://huggingface.co/MathGenie/MathGenie-InterLM-20B) |
+| Mixtral-8x7B | [MathGenie-Mixtral-8x7B](https://huggingface.co/MathGenie/MathGenie-Mixtral-8x7B) |
 ### Inference & Evaluation