Instructions to use Multi-Domain-Expert-Learning/expert-pubmed_central with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Multi-Domain-Expert-Learning/expert-pubmed_central with Transformers:

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

pipe = pipeline("text-generation", model="Multi-Domain-Expert-Learning/expert-pubmed_central")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Multi-Domain-Expert-Learning/expert-pubmed_central")
model = AutoModelForCausalLM.from_pretrained("Multi-Domain-Expert-Learning/expert-pubmed_central", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Multi-Domain-Expert-Learning/expert-pubmed_central with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Multi-Domain-Expert-Learning/expert-pubmed_central"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Multi-Domain-Expert-Learning/expert-pubmed_central",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Multi-Domain-Expert-Learning/expert-pubmed_central

SGLang

How to use Multi-Domain-Expert-Learning/expert-pubmed_central 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 "Multi-Domain-Expert-Learning/expert-pubmed_central" \
    --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": "Multi-Domain-Expert-Learning/expert-pubmed_central",
		"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 "Multi-Domain-Expert-Learning/expert-pubmed_central" \
        --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": "Multi-Domain-Expert-Learning/expert-pubmed_central",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Multi-Domain-Expert-Learning/expert-pubmed_central with Docker Model Runner:
```
docker model run hf.co/Multi-Domain-Expert-Learning/expert-pubmed_central
```

expert-pubmed_central

Commit History

Sep 11, 2023

22:41
Librarian Bot: Add base_model information to model⁠

librarian-bot

e737e05

May 4, 2023

00:43
commit files to HF hub

P1ayer-1

0e1925d
00:42
update model card README.md

P1ayer-1

5ae72ce
00:40
Model save

P1ayer-1

b1aa28b
00:39
Training in progress, step 1000

P1ayer-1

5b5e241
00:26
Training in progress, step 500

P1ayer-1

2bb79b2

May 3, 2023

19:14
initial commit

P1ayer-1

7670ea0

Commit History

Sep 11, 2023

Librarian Bot: Add base_model information to model⁠

May 4, 2023

commit files to HF hub

update model card README.md

Model save

Training in progress, step 1000

Training in progress, step 500

May 3, 2023

initial commit