Instructions to use Wanfq/Explore-LM-Ext-7B-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Wanfq/Explore-LM-Ext-7B-Math with Transformers:

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

pipe = pipeline("text-generation", model="Wanfq/Explore-LM-Ext-7B-Math")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Wanfq/Explore-LM-Ext-7B-Math")
model = AutoModelForCausalLM.from_pretrained("Wanfq/Explore-LM-Ext-7B-Math")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Wanfq/Explore-LM-Ext-7B-Math with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Wanfq/Explore-LM-Ext-7B-Math"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Wanfq/Explore-LM-Ext-7B-Math",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Wanfq/Explore-LM-Ext-7B-Math

SGLang

How to use Wanfq/Explore-LM-Ext-7B-Math 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 "Wanfq/Explore-LM-Ext-7B-Math" \
    --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": "Wanfq/Explore-LM-Ext-7B-Math",
		"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 "Wanfq/Explore-LM-Ext-7B-Math" \
        --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": "Wanfq/Explore-LM-Ext-7B-Math",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Wanfq/Explore-LM-Ext-7B-Math with Docker Model Runner:
```
docker model run hf.co/Wanfq/Explore-LM-Ext-7B-Math
```

Wanfq commited on Oct 16, 2023

Commit

239e3d8

1 Parent(s): 8b966ce

Update README.md

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@@ -11,7 +11,7 @@ language:
 **Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration**
-<h4> |<a href="https://arxiv.org/abs/2305.xxxxx"> 📑 Paper </a> |
 <a href="https://huggingface.co/datasets?sort=trending&search=Explore_Instruct"> 🤗 Data </a> |
 <a href="https://huggingface.co/models?sort=trending&search=Explore-LM"> 🤗 Model </a> |
 <a href="https://github.com/fanqiwan/Explore-Instruct"> 🐱 Github Repo </a> |
@@ -307,7 +307,7 @@ If you find this work is relevant with your research or applications, please fee
    title={Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration},
    author={Fanqi, Wan and Xinting, Huang and Tao, Yang and Xiaojun, Quan and Wei, Bi and Shuming, Shi},
    year={2023},
-   eprint={2305.xxxxx},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
 }

 **Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration**
+<h4> |<a href="https://arxiv.org/abs/2310.09168"> 📑 Paper </a> |
 <a href="https://huggingface.co/datasets?sort=trending&search=Explore_Instruct"> 🤗 Data </a> |
 <a href="https://huggingface.co/models?sort=trending&search=Explore-LM"> 🤗 Model </a> |
 <a href="https://github.com/fanqiwan/Explore-Instruct"> 🐱 Github Repo </a> |
    title={Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration},
    author={Fanqi, Wan and Xinting, Huang and Tao, Yang and Xiaojun, Quan and Wei, Bi and Shuming, Shi},
    year={2023},
+   eprint={2310.09168},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
 }