# Huggingface Skills > Hugging Face Hub operations - models, datasets, Spaces, training with TRL, evaluation, and CLI operations. Use when working with ML models, datasets, or Hugging Face infrastructure. Use when this capability is needed. - Skill: `tomevault-io/huggingface-skills` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/huggingface-skills` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/huggingface-skills/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/huggingface-skills --- # Hugging Face Skills ## When to Use This Skill - Uploading/downloading models and datasets - Creating or managing Spaces - Training models with TRL (SFT, DPO, GRPO) - Running model evaluations - Using the Hugging Face CLI - Converting models to GGUF format - Tracking ML experiments ## CLI Operations ### Setup ```bash # Install pip install huggingface_hub[cli] # Login huggingface-cli login # Check identity huggingface-cli whoami ``` ### Repository Operations ```bash # Create repo huggingface-cli repo create my-model --type model huggingface-cli repo create my-dataset --type dataset huggingface-cli repo create my-space --type space # Clone git clone https://huggingface.co/username/my-model # Upload huggingface-cli upload username/my-model ./local_dir huggingface-cli upload username/my-model ./file.safetensors # Download huggingface-cli download username/my-model huggingface-cli download username/my-model --include "*.safetensors" ``` ## Python API ### Upload Models ```python from huggingface_hub import HfApi, upload_folder api = HfApi() # Create repo api.create_repo("my-model", repo_type="model", private=False) # Upload entire folder upload_folder( folder_path="./model_output", repo_id="username/my-model", repo_type="model", ) # Upload single file api.upload_file( path_or_fileobj="./model.safetensors", path_in_repo="model.safetensors", repo_id="username/my-model", ) ``` ### Download Models ```python from huggingface_hub import snapshot_download, hf_hub_download # Download entire repo snapshot_download(repo_id="meta-llama/Llama-3.2-3B") # Download specific file hf_hub_download( repo_id="meta-llama/Llama-3.2-3B", filename="config.json", ) # With cache from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B") ``` ## Datasets ### Create Dataset ```python from datasets import Dataset, DatasetDict # From dict data = { "text": ["Hello world", "How are you?"], "label": [0, 1], } dataset = Dataset.from_dict(data) # From pandas import pandas as pd df = pd.read_csv("data.csv") dataset = Dataset.from_pandas(df) # Create train/test split dataset = dataset.train_test_split(test_size=0.1) # Upload dataset.push_to_hub("username/my-dataset") ``` ### Load Dataset ```python from datasets import load_dataset # From Hub dataset = load_dataset("username/my-dataset") # Specific split train = load_dataset("username/my-dataset", split="train") # Streaming for large datasets dataset = load_dataset("username/my-dataset", streaming=True) # With SQL query dataset = load_dataset("username/my-dataset", split="train[:1000]") ``` ## Training with TRL ### Supervised Fine-Tuning (SFT) ```python from trl import SFTTrainer from transformers import AutoModelForCausalLM, AutoTokenizer from datasets import load_dataset model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B") tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B") dataset = load_dataset("your-dataset", split="train") trainer = SFTTrainer( model=model, train_dataset=dataset, tokenizer=tokenizer, max_seq_length=2048, dataset_text_field="text", args=TrainingArguments( output_dir="./sft_output", per_device_train_batch_size=4, gradient_accumulation_steps=4, learning_rate=2e-5, num_train_epochs=3, logging_steps=10, save_steps=100, push_to_hub=True, ), ) trainer.train() ``` ### DPO Training ```python from trl import DPOTrainer, DPOConfig trainer = DPOTrainer( model=model, ref_model=None, # Uses implicit reference args=DPOConfig( output_dir="./dpo_output", beta=0.1, per_device_train_batch_size=4, ), train_dataset=dataset, # Must have "chosen" and "rejected" columns tokenizer=tokenizer, ) trainer.train() ``` ### GRPO Training ```python from trl import GRPOTrainer, GRPOConfig config = GRPOConfig( output_dir="./grpo_output", per_device_train_batch_size=8, learning_rate=1e-6, ) trainer = GRPOTrainer( model=model, config=config, train_dataset=dataset, reward_model=reward_model, ) trainer.train() ``` ## Model Evaluation ```python from lighteval.tasks import Task from lighteval.metrics import Accuracy # Define evaluation task task = Task( name="my_eval", prompt_template="Question: {question}\nAnswer:", gold_key="answer", metric=Accuracy(), ) # Run evaluation from lighteval import evaluate results = evaluate( model="username/my-model", tasks=[task], output_dir="./eval_results", ) ``` ## GGUF Conversion ```bash # Install llama.cpp git clone https://github.com/ggerganov/llama.cpp cd llama.cpp && make # Convert to GGUF python convert_hf_to_gguf.py /path/to/model --outtype f16 # Quantize ./llama-quantize model.gguf model-q4_k_m.gguf Q4_K_M ``` ## Spaces ### Create Space ```bash # Create gradio space huggingface-cli repo create my-app --type space --space_sdk gradio # Or streamlit huggingface-cli repo create my-app --type space --space_sdk streamlit ``` ### Gradio App Example ```python # app.py import gradio as gr from transformers import pipeline pipe = pipeline("text-generation", model="gpt2") def generate(prompt): return pipe(prompt, max_length=100)[0]["generated_text"] demo = gr.Interface(fn=generate, inputs="text", outputs="text") demo.launch() ``` ## Experiment Tracking ```python from huggingface_hub import create_repo, upload_file import json # Log metrics metrics = {"loss": 0.5, "accuracy": 0.92} with open("metrics.json", "w") as f: json.dump(metrics, f) upload_file( path_or_fileobj="metrics.json", path_in_repo="runs/exp_001/metrics.json", repo_id="username/my-model", ) ``` ## Best Practices - [ ] Use .safetensors format (safer than pickle) - [ ] Include model card (README.md) with usage examples - [ ] Tag models with appropriate metadata - [ ] Use Git LFS for large files - [ ] Set appropriate licenses - [ ] Include requirements.txt for Spaces - [ ] Use streaming for large datasets --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/allanninal) — claim your Tome and manage your conversions.