Instructions to use CornCube/my_awesome_eli5_clm-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CornCube/my_awesome_eli5_clm-model with Transformers:

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

pipe = pipeline("text-generation", model="CornCube/my_awesome_eli5_clm-model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CornCube/my_awesome_eli5_clm-model")
model = AutoModelForCausalLM.from_pretrained("CornCube/my_awesome_eli5_clm-model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CornCube/my_awesome_eli5_clm-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CornCube/my_awesome_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": "CornCube/my_awesome_eli5_clm-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CornCube/my_awesome_eli5_clm-model

SGLang

How to use CornCube/my_awesome_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 "CornCube/my_awesome_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": "CornCube/my_awesome_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 "CornCube/my_awesome_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": "CornCube/my_awesome_eli5_clm-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CornCube/my_awesome_eli5_clm-model with Docker Model Runner:
```
docker model run hf.co/CornCube/my_awesome_eli5_clm-model
```

my_awesome_eli5_clm-model / training_args.bin

CornCube

Training in progress, step 500

1cbec47 verified over 2 years ago

History Blame Contribute Delete

4.98 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 7bdb595b9488a94d50de14fcceb41ac348d2d8a4248790115495f739c5c36a0a
Size of remote file:: 4.98 kB
SHA256:: 6292b37b8e10ab59132e091683a940856a459d3388b2efab3d672d6848ef93a1

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

CornCube
/

my_awesome_eli5_clm-model

Detected Pickle imports (9)

Xet Pointer Details