Instructions to use hematteo/readout-recipe-control with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hematteo/readout-recipe-control with Transformers:

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

pipe = pipeline("text-generation", model="hematteo/readout-recipe-control")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hematteo/readout-recipe-control", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hematteo/readout-recipe-control with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hematteo/readout-recipe-control

SGLang

How to use hematteo/readout-recipe-control 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 "hematteo/readout-recipe-control" \
    --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": "hematteo/readout-recipe-control",
		"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 "hematteo/readout-recipe-control" \
        --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": "hematteo/readout-recipe-control",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hematteo/readout-recipe-control with Docker Model Runner:
```
docker model run hf.co/hematteo/readout-recipe-control
```

Add library name and pipeline tag, remove arxiv tag

by nielsr HF Staff - opened 12 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-2

nielsr

12 days ago

This PR adds the library_name and pipeline_tag metadata fields to enable the proper "Use in Transformers" button and proper filtering. It also removes the arxiv tag from the metadata, since the arXiv ID should not be included in the YAML metadata section. The arXiv link remains in the Markdown content.

Add library name and pipeline tag, remove arxiv taga7297787

hematteo changed pull request status to merged 11 days ago

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