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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

leaderboard-pr-bot commited on Nov 17, 2023

Commit

5d142dd

1 Parent(s): 2e868c4

Adding Evaluation Results

Browse files

This is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr

The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.

If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions

Files changed (1) hide show

README.md +14 -0

README.md CHANGED Viewed

@@ -107,3 +107,17 @@ Despite this, we have still worked hard to obtain opening the weights of the mod
 Our researchers have no authority to publicly release them without authorization.
 Thank you for your understanding.

 Our researchers have no authority to publicly release them without authorization.
 Thank you for your understanding.
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_WizardLM__WizardLM-70B-V1.0)
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 57.17   |
+| ARC (25-shot)         | 65.44          |
+| HellaSwag (10-shot)   | 84.41    |
+| MMLU (5-shot)         | 64.05         |
+| TruthfulQA (0-shot)   | 54.81   |
+| Winogrande (5-shot)   | 80.82   |
+| GSM8K (5-shot)        | 17.97        |
+| DROP (3-shot)         | 32.71         |