Instructions to use WizardLMTeam/WizardMath-70B-V1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WizardLMTeam/WizardMath-70B-V1.0 with Transformers:

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

pipe = pipeline("text-generation", model="WizardLMTeam/WizardMath-70B-V1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WizardLMTeam/WizardMath-70B-V1.0")
model = AutoModelForCausalLM.from_pretrained("WizardLMTeam/WizardMath-70B-V1.0")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WizardLMTeam/WizardMath-70B-V1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WizardLMTeam/WizardMath-70B-V1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WizardLMTeam/WizardMath-70B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WizardLMTeam/WizardMath-70B-V1.0

SGLang

How to use WizardLMTeam/WizardMath-70B-V1.0 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 "WizardLMTeam/WizardMath-70B-V1.0" \
    --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": "WizardLMTeam/WizardMath-70B-V1.0",
		"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 "WizardLMTeam/WizardMath-70B-V1.0" \
        --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": "WizardLMTeam/WizardMath-70B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WizardLMTeam/WizardMath-70B-V1.0 with Docker Model Runner:
```
docker model run hf.co/WizardLMTeam/WizardMath-70B-V1.0
```

Update README.md

#13

by haipeng1 - opened Sep 1, 2023

base: refs/heads/main

←

from: refs/pr/13

Discussion Files changed

-4

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -75,6 +75,10 @@ Please use **the same systems prompts strictly** with us, and we do not guarante
 "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response: Let's think step by step."
 ```
 ❗<b>To commen concern about dataset:</b>
@@ -84,10 +88,6 @@ Our researchers have no authority to publicly release them without authorization
 Thank you for your understanding.
-## Inference Demo Script
-We provide the inference demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo).
 ## Citation
 Please cite the repo if you use the data, method or code in this repo.

 "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response: Let's think step by step."
 ```
+## Inference WizardMath Demo Script
+We provide the WizardMath inference demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo).
 ❗<b>To commen concern about dataset:</b>
 Thank you for your understanding.
 ## Citation
 Please cite the repo if you use the data, method or code in this repo.