Instructions to use hematteo/readout-recipe-control with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Commit History
11 days ago
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Add library name and pipeline tag, remove arxiv tag (#1)
- Add library name and pipeline tag, remove arxiv tag (a7297787162732a234e522cc628ac1b187aa17ef) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>ab70821
12 days ago
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docs: link the project page at matteohe.com/learning-to-read-out
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docs: add quick starts and links to the atlas Space and collection
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docs: link the arXiv preprint (2610.21025) and the NeurIPS 2026 citation
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Replace the step-512 pilot pull with the complete 10B-token production runs (5 arms, 16 checkpoints each)
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Remove stray macOS AppleDouble cache file
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Add files using upload-large-folder tool
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Add files using upload-large-folder tool
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initial commit
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