Instructions to use WizardLMTeam/WizardMath-7B-V1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WizardLMTeam/WizardMath-7B-V1.1 with Transformers:

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

pipe = pipeline("text-generation", model="WizardLMTeam/WizardMath-7B-V1.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WizardLMTeam/WizardMath-7B-V1.1")
model = AutoModelForCausalLM.from_pretrained("WizardLMTeam/WizardMath-7B-V1.1", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WizardLMTeam/WizardMath-7B-V1.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WizardLMTeam/WizardMath-7B-V1.1"
# 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-7B-V1.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WizardLMTeam/WizardMath-7B-V1.1

SGLang

How to use WizardLMTeam/WizardMath-7B-V1.1 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 "WizardLMTeam/WizardMath-7B-V1.1" \
    --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": "WizardLMTeam/WizardMath-7B-V1.1",
		"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 "WizardLMTeam/WizardMath-7B-V1.1" \
        --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": "WizardLMTeam/WizardMath-7B-V1.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WizardLMTeam/WizardMath-7B-V1.1 with Docker Model Runner:
```
docker model run hf.co/WizardLMTeam/WizardMath-7B-V1.1
```

WizardLM commited on Dec 19, 2023

Commit

c6326e9

1 Parent(s): caf588a

Update README.md

Browse files

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README.md +3 -3

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@@ -25,7 +25,7 @@ pipeline_tag: text-generation
 ## News
-[12/19/2023] 🔥 We released **WizardMath-7B-V1.1**, the **SOTA 7B math LLM**, achieves **83.2 pass@1** on GSM8k, and **33.0 pass@1** on MATH.
 [12/19/2023] 🔥 **WizardMath-7B-V1.1** outperforms **ChatGPT 3.5**, **Gemini Pro**, **Mixtral MOE**, and **Claude Instant** on GSM8K pass@1.
@@ -45,9 +45,9 @@ pipeline_tag: text-generation
 | ----- |------| ---- |
 | MPT-7B              | 6.8          | 3.0         |
 |Llama 1-7B          | 11.0         | 2.9         |
-|Llama 2-7b|12.3	|2.8	|
 |Yi-6b| 32.6	|5.8	|
-|Mistral-7b|37.8	|9.1	|
 |Qwen-7b|47.8	|9.3	|
 | RFT-7B              | 50.3         | --          |
 | MAmmoTH-7B (COT)    | 50.5         | 10.4        |

 ## News
+[12/19/2023] 🔥 We released **WizardMath-7B-V1.1** trained from Mistral-7B, the **SOTA 7B math LLM**, achieves **83.2 pass@1** on GSM8k, and **33.0 pass@1** on MATH.
 [12/19/2023] 🔥 **WizardMath-7B-V1.1** outperforms **ChatGPT 3.5**, **Gemini Pro**, **Mixtral MOE**, and **Claude Instant** on GSM8K pass@1.
 | ----- |------| ---- |
 | MPT-7B              | 6.8          | 3.0         |
 |Llama 1-7B          | 11.0         | 2.9         |
+|Llama 2-7B|12.3	|2.8	|
 |Yi-6b| 32.6	|5.8	|
+|Mistral-7B|37.8	|9.1	|
 |Qwen-7b|47.8	|9.3	|
 | RFT-7B              | 50.3         | --          |
 | MAmmoTH-7B (COT)    | 50.5         | 10.4        |