Hugging Face setup

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Prerequisites

• Hugging Face account: huggingface.co
• Personal Access Token with read access: huggingface.co/settings/tokens
• huggingface_hub, transformers, and safetensors installed

pip install --upgrade huggingface_hub transformers safetensors

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Create a model repository

• UI: huggingface.co/new
• CLI:

huggingface-cli login
huggingface-cli repo create <namespace>/<model-name> --type model

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Required files

• Model weights, including Safetensors. Custom code must be loadable by Transformers, Diffusers, or vLLM in the standard way.
• Model configuration files (for example, config.json)
• Tokenizer files (for example, tokenizer.json, tokenizer_config.json, vocab.json, merges.txt)
• Any processors (for example, preprocessor_config.json, image processor files)
• A README.md with a pipeline_tag

• Transformers docs
• Safetensors format

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Push model weights and artifacts

from huggingface_hub import login
from transformers import AutoModelForCausalLM, AutoTokenizer

login(token="<HF_TOKEN>")

model_id = "<namespace>/<model-name>"

model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

model.save_pretrained("./model", safe_serialization=True)
tokenizer.save_pretrained("./model")

model.push_to_hub(model_id, private=True)
tokenizer.push_to_hub(model_id, private=True)

cp -r ./model/* .
git add .
git commit -m "Add model weights, config, and tokenizer"
git push

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Verify the repository

• Confirm all required files are present (weights, config, tokenizer, processors).
• Ensure README.md includes a pipeline tag:

pipeline_tag: text-generation