Instructions to use vanillaOVO/WizardLM-7B-V1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vanillaOVO/WizardLM-7B-V1.0 with Transformers:

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

pipe = pipeline("text-generation", model="vanillaOVO/WizardLM-7B-V1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vanillaOVO/WizardLM-7B-V1.0")
model = AutoModelForCausalLM.from_pretrained("vanillaOVO/WizardLM-7B-V1.0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vanillaOVO/WizardLM-7B-V1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vanillaOVO/WizardLM-7B-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": "vanillaOVO/WizardLM-7B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vanillaOVO/WizardLM-7B-V1.0

SGLang

How to use vanillaOVO/WizardLM-7B-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 "vanillaOVO/WizardLM-7B-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": "vanillaOVO/WizardLM-7B-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 "vanillaOVO/WizardLM-7B-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": "vanillaOVO/WizardLM-7B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vanillaOVO/WizardLM-7B-V1.0 with Docker Model Runner:
```
docker model run hf.co/vanillaOVO/WizardLM-7B-V1.0
```

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Check out the documentation for more information.

Note

This is a replica of the official repository, intended solely for research purposes to replicate results. If there are any copyright issues, please contact me.

The WizardLM delta weights.

WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions

🤗 HF Repo • 🐦 Twitter • 📃 [WizardLM] • 📃 [WizardCoder] • 📃 [WizardMath]

👋 Join our Discord

Model	Checkpoint	Paper	HumanEval	MBPP	Demo	License
WizardCoder-Python-34B-V1.0	🤗 HF Link	📃 [WizardCoder]	73.2	61.2	Demo	Llama2
WizardCoder-15B-V1.0	🤗 HF Link	📃 [WizardCoder]	59.8	50.6	--	OpenRAIL-M
WizardCoder-Python-13B-V1.0	🤗 HF Link	📃 [WizardCoder]	64.0	55.6	--	Llama2
WizardCoder-3B-V1.0	🤗 HF Link	📃 [WizardCoder]	34.8	37.4	Demo	OpenRAIL-M
WizardCoder-1B-V1.0	🤗 HF Link	📃 [WizardCoder]	23.8	28.6	--	OpenRAIL-M

Model	Checkpoint	Paper	GSM8k	MATH	Online Demo	License
WizardMath-70B-V1.0	🤗 HF Link	📃 [WizardMath]	81.6	22.7	Demo	Llama 2
WizardMath-13B-V1.0	🤗 HF Link	📃 [WizardMath]	63.9	14.0	Demo	Llama 2
WizardMath-7B-V1.0	🤗 HF Link	📃 [WizardMath]	54.9	10.7	Demo	Llama 2

^Model	^Checkpoint	^Paper	^MT-Bench	^AlpacaEval	^WizardEval	^HumanEval	^License
^{WizardLM-13B-V1.2}	^{🤗 HF Link}		^7.06	^89.17%	^101.4%	^{36.6 pass@1}	^{Llama 2 License}
^{WizardLM-13B-V1.1}	^{🤗 HF Link}		^6.76	^86.32%	^99.3%	^{25.0 pass@1}	^{Non-commercial}
^{WizardLM-30B-V1.0}	^{🤗 HF Link}		^7.01		^97.8%	^{37.8 pass@1}	^{Non-commercial}
^{WizardLM-13B-V1.0}	^{🤗 HF Link}		^6.35	^75.31%	^89.1%	^{24.0 pass@1}	^{Non-commercial}
^{WizardLM-7B-V1.0}	^{🤗 HF Link}	^{📃 [WizardLM]}			^78.0%	^{19.1 pass@1}	^{Non-commercial}

Inference WizardLM Demo Script

We provide the inference WizardLM demo code here.

Downloads last month: 5

Collection including vanillaOVO/WizardLM-7B-V1.0

Wizard Models

Collection

Replica of the official repository for research purposes • 6 items • Updated Jun 20, 2024 • 1

Papers for vanillaOVO/WizardLM-7B-V1.0

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Paper • 2308.09583 • Published Aug 18, 2023 • 8

WizardCoder: Empowering Code Large Language Models with Evol-Instruct

Paper • 2306.08568 • Published Jun 14, 2023 • 34

WizardLM: Empowering Large Language Models to Follow Complex Instructions

Paper • 2304.12244 • Published Apr 24, 2023 • 14