Instructions to use WilliamStar/eli5_clm-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WilliamStar/eli5_clm-model with Transformers:

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

pipe = pipeline("text-generation", model="WilliamStar/eli5_clm-model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WilliamStar/eli5_clm-model")
model = AutoModelForCausalLM.from_pretrained("WilliamStar/eli5_clm-model")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WilliamStar/eli5_clm-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WilliamStar/eli5_clm-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WilliamStar/eli5_clm-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WilliamStar/eli5_clm-model

SGLang

How to use WilliamStar/eli5_clm-model 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 "WilliamStar/eli5_clm-model" \
    --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": "WilliamStar/eli5_clm-model",
		"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 "WilliamStar/eli5_clm-model" \
        --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": "WilliamStar/eli5_clm-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WilliamStar/eli5_clm-model with Docker Model Runner:
```
docker model run hf.co/WilliamStar/eli5_clm-model
```

eli5_clm-model

Commit History

Adding `safetensors` variant of this model

b9ace84
verified

SFconvertbot commited on May 1, 2025

Training in progress, step 3500

c546f48

Anda Liang commited on Apr 27, 2024

Training in progress, step 3000

990b212

Anda Liang commited on Apr 27, 2024

Training in progress, step 2500

44482b7

Anda Liang commited on Apr 27, 2024

Training in progress, step 2000

aff475c

Anda Liang commited on Apr 27, 2024

Training in progress, step 1500

698b80a

Anda Liang commited on Apr 27, 2024

Training in progress, step 1000

196a42c

Anda Liang commited on Apr 27, 2024

Training in progress, step 500

291d599

Anda Liang commited on Apr 27, 2024

initial commit

72f6ec5
verified

WilliamStar commited on Apr 27, 2024

Commit History

Adding `safetensors` variant of this model b9ace84 verified

Training in progress, step 3500 c546f48

Training in progress, step 3000 990b212

Training in progress, step 2500 44482b7

Training in progress, step 2000 aff475c

Training in progress, step 1500 698b80a

Training in progress, step 1000 196a42c

Training in progress, step 500 291d599

initial commit 72f6ec5 verified

Adding `safetensors` variant of this model

b9ace84
verified

Training in progress, step 3500

c546f48

Training in progress, step 3000

990b212

Training in progress, step 2500

44482b7

Training in progress, step 2000

aff475c

Training in progress, step 1500

698b80a

Training in progress, step 1000

196a42c

Training in progress, step 500

291d599

initial commit

72f6ec5
verified