Instructions to use mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop with Transformers:

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

pipe = pipeline("text-generation", model="mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop")
model = AutoModelForCausalLM.from_pretrained("mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop

SGLang

How to use mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop 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 "mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop" \
    --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": "mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop",
		"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 "mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop" \
        --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": "mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop with Docker Model Runner:
```
docker model run hf.co/mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop
```

GuaPeTe-2-tiny-finetuned-eubookshop / pytorch_model.bin

mrm8488

Initial commit from mrm8488

a404dd2 over 5 years ago

History Blame Contribute Delete

499 MB

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:: 1a316717a9d978efaf1bc7d1f2e55457fb4c5dccebdd873d6b42949082f27210
Size of remote file:: 499 MB
SHA256:: 619ef416f786b72caaa90d265b71a06bcfba66ceee597489d55c15ffc0adca6b

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

mrm8488
/

GuaPeTe-2-tiny-finetuned-eubookshop

Detected Pickle imports (4)

Xet Pointer Details