Instructions to use YenugulaAIML/scaleop-pythia-conversions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YenugulaAIML/scaleop-pythia-conversions with Transformers:

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

pipe = pipeline("text-generation", model="YenugulaAIML/scaleop-pythia-conversions")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("YenugulaAIML/scaleop-pythia-conversions", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use YenugulaAIML/scaleop-pythia-conversions with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/YenugulaAIML/scaleop-pythia-conversions

SGLang

How to use YenugulaAIML/scaleop-pythia-conversions 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 "YenugulaAIML/scaleop-pythia-conversions" \
    --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": "YenugulaAIML/scaleop-pythia-conversions",
		"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 "YenugulaAIML/scaleop-pythia-conversions" \
        --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": "YenugulaAIML/scaleop-pythia-conversions",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use YenugulaAIML/scaleop-pythia-conversions with Docker Model Runner:
```
docker model run hf.co/YenugulaAIML/scaleop-pythia-conversions
```

Add library and pipeline tags

by nielsr HF Staff - opened 2 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-5

nielsr

2 days ago

This PR adds the missing library_name: transformers and pipeline_tag: text-generation metadata fields to the model card. The repository contains GPTNeoXForCausalLM checkpoints that are used with the transformers library, and the model performs text generation, so these fields improve discoverability and enable the correct built-in usage snippet on the Hub.

Add library and pipeline tags23aa7ce3

YenugulaAIML

Owner 1 day ago

Thanks for checking it

YenugulaAIML changed pull request status to merged 1 day ago

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