Instructions to use osidenna/SoftwareRequirements-T5-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use osidenna/SoftwareRequirements-T5-Base with Transformers:

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

pipe = pipeline("text-generation", model="osidenna/SoftwareRequirements-T5-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("osidenna/SoftwareRequirements-T5-Base")
model = AutoModelForSeq2SeqLM.from_pretrained("osidenna/SoftwareRequirements-T5-Base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use osidenna/SoftwareRequirements-T5-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "osidenna/SoftwareRequirements-T5-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "osidenna/SoftwareRequirements-T5-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/osidenna/SoftwareRequirements-T5-Base

SGLang

How to use osidenna/SoftwareRequirements-T5-Base 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 "osidenna/SoftwareRequirements-T5-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "osidenna/SoftwareRequirements-T5-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "osidenna/SoftwareRequirements-T5-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "osidenna/SoftwareRequirements-T5-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use osidenna/SoftwareRequirements-T5-Base with Docker Model Runner:
```
docker model run hf.co/osidenna/SoftwareRequirements-T5-Base
```

SoftwareRequirements-T5-Base

Commit History

Adding `safetensors` variant of this model

6caaf54

SFconvertbot commited on Aug 3, 2023

Update README.md

0d8b598

Oumoukelthoum sidenna commited on Aug 2, 2023

Update README.md

fbf9ebc

Oumoukelthoum sidenna commited on Aug 2, 2023

Update README.md

c08dae7

Oumoukelthoum sidenna commited on Jul 31, 2023

uploading model files

0ceee33

Oumoukelthoum sidenna commited on Jul 31, 2023

initial commit

ef54dcf

osidenna commited on Jul 31, 2023

Commit History

Adding `safetensors` variant of this model 6caaf54

Update README.md 0d8b598

Update README.md fbf9ebc

Update README.md c08dae7

uploading model files 0ceee33