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Hugging Face Hub operations, model inference, dataset management, PEFT/LoRA fine-tuning, and Spaces deployment via MCP tools and Python APIs
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/oimiragieo/agent-studio --skill huggingfaceコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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Agent frontmatter enhancements — disallowedTools, mcpServers scoping, fork_eligible field
Context management — pre-compact persistence, threshold alignment, microcompact/circuit breaker detection
Hook enhancements — updatedInput for bash safety prefixes, suppressOutput for verbose blocks, denial-based routing feedback
SKILL.md を表示中
SOC 職業分類に基づく
| name | huggingface |
| description | Hugging Face Hub operations, model inference, dataset management, PEFT/LoRA fine-tuning, and Spaces deployment via MCP tools and Python APIs |
| version | 1.0.0 |
| model | sonnet |
| invoked_by | both |
| user_invocable | true |
| tools | ["Read","Write","Bash","WebFetch","Skill"] |
| agents | ["developer","python-pro","researcher"] |
| category | External Integrations |
| tags | ["huggingface","llm","ml","transformers","datasets","inference","fine-tuning","peft","lora","spaces"] |
| source | builtin |
| trust_score | 100 |
| provenance_sha | b9c9f0f8263cfe4f |
Provides structured workflows for interacting with the Hugging Face ecosystem: Hub repository search, model loading and inference, dataset management, PEFT/LoRA fine-tuning, and Spaces deployment. Integrates via MCP tools when available and falls back to Python APIs and the huggingface_hub CLI.
Skill({ skill: 'huggingface' });
Invoke when:
datasets libraryWhen the Hugging Face MCP server is available, prefer MCP tools over direct API calls.
Search for models by task or keyword:
// Search models
mcp__claude_ai_Hugging_Face__hub_repo_search({
query: 'text-classification',
repo_type: 'model',
limit: 10,
});
// Get detailed repo info
mcp__claude_ai_Hugging_Face__hub_repo_details({
repo_id: 'bert-base-uncased',
repo_type: 'model',
});
Expected output: JSON list of repos with modelId, downloads, likes, tags, pipeline_tag, and lastModified.
Verify: Confirm pipeline_tag matches the intended task before proceeding.
// Search arXiv papers on the Hub
({
: ,
: ,
});
({
: ,
: ,
});
// Search HF docs
mcp__claude_ai_Hugging_Face__hf_doc_search({
query: 'AutoModelForCausalLM from_pretrained',
library: 'transformers',
});
from transformers import pipeline
# Text generation
generator = pipeline(
"text-generation",
model="meta-llama/Llama-3.2-1B-Instruct",
device_map="auto",
torch_dtype="auto",
)
result = generator("What is the capital of France?", max_new_tokens=100)
print(result[0]["generated_text"])
Verify: result[0]["generated_text"] contains coherent continuation. Check device_map resolves to GPU if available.
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "microsoft/Phi-3-mini-4k-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
attn_implementation="flash_attention_2", # if available
)
inputs = tokenizer("Hello, world!", return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
from huggingface_hub import InferenceClient
client = InferenceClient(token="hf_...") # or use HF_TOKEN env var
# Text generation
result = client.text_generation(
"Tell me a joke",
model="mistralai/Mistral-7B-Instruct-v0.3",
max_new_tokens=200,
)
print(result)
# Chat completion (OpenAI-compatible)
response = client.chat_completion(
model="meta-llama/Llama-3.1-8B-Instruct",
messages=[{"role": "user", "content": "Explain quantum entanglement"}],
max_tokens=300,
)
print(response.choices[0].message.content)
Verify: Check response.choices[0].finish_reason == "stop" for complete generation.
from datasets import load_dataset
# Public dataset
ds = load_dataset("squad", split="train")
print(ds.column_names) # ['id', 'title', 'context', 'question', 'answers']
# With streaming (large datasets)
ds_stream = load_dataset("allenai/c4", "en", split="train", streaming=True)
sample = next(iter(ds_stream))
# From Hub with specific config
ds = load_dataset("glue", "mrpc", split={"train": "train", "val": "validation"})
Verify: len(ds) returns expected row count. ds.features shows correct schema.
from datasets import Dataset, DatasetDict
from huggingface_hub import HfApi
# Create from dict
data = {"text": ["Hello", "World"], "label": [0, 1]}
ds = Dataset.from_dict(data)
# Push to Hub
ds.push_to_hub("username/my-dataset", private=True, token="hf_...")
# Verify upload
api = HfApi()
info = api.dataset_info("username/my-dataset")
print(f"Rows: {info.cardData.get('dataset_info', {}).get('splits', {})}")
from datasets import load_dataset
from transformers import AutoTokenizer
ds = load_dataset("imdb", split="train")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
def tokenize(batch):
return tokenizer(batch["text"], truncation=True, padding="max_length", max_length=512)
ds_tokenized = ds.map(tokenize, batched=True, remove_columns=["text"])
ds_tokenized.set_format(type="torch", columns=["input_ids", "attention_mask", "label"])
from peft import LoraConfig, get_peft_model, TaskType
lora_config = LoraConfig(
r=16, # rank — higher = more parameters
lora_alpha=32, # scaling factor
target_modules=["q_proj", "v_proj"], # layers to apply LoRA to
lora_dropout=0.05,
bias="none",
task_type=TaskType.CAUSAL_LM,
)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# Expected: trainable params: ~X MB / total: ~Y GB (<1% of total)
from transformers import BitsAndBytesConfig, AutoModelForCausalLM
from peft import prepare_model_for_kbit_training
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype="bfloat16",
bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B",
quantization_config=bnb_config,
device_map="auto",
)
model = prepare_model_for_kbit_training(model)
Use conversational message format for modern SFT workflows:
from trl import SFTTrainer, SFTConfig
from datasets import load_dataset
# 2025: Use conversational format with system prompt
dataset = load_dataset("HuggingFaceH4/ultrachat_200k", split="train_sft")
training_args = SFTConfig(
output_dir="./sft-output",
num_train_epochs=3,
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
learning_rate=2e-4,
bf16=True, # prefer bf16 over fp16 for stability
logging_steps=10,
save_strategy="epoch",
# Performance: pack sequences to fill context window (reduces padding waste)
packing=True,
max_seq_length=2048,
# Train only on assistant responses, not on user/system tokens
dataset_kwargs={"skip_prepare_dataset": False},
push_to_hub=True,
hub_model_id="username/my-finetuned-model",
)
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=dataset,
peft_config=lora_config,
)
trainer.train()
trainer.push_to_hub()
Performance Optimization (Liger Kernels + Flash Attention):
# Significant speedup for supported architectures (Llama, Mistral, Phi)
from liger_kernel.transformers import apply_liger_kernel_to_llama
# Apply before model loading
apply_liger_kernel_to_llama()
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B",
torch_dtype=torch.bfloat16,
device_map="auto",
attn_implementation="flash_attention_2", # 2-4x throughput improvement
)
Verify: Loss decreases from epoch 1 to 3. trainer.state.log_history[-1]["train_loss"] should be < initial loss. Training time with packing + Flash Attention + Liger Kernels is typically 5-20x faster than naive baseline.
from peft import PeftModel
# Load base + adapter
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B")
model = PeftModel.from_pretrained(base_model, "username/my-lora-adapter")
# Merge and unload (creates standalone model)
merged = model.merge_and_unload()
merged.save_pretrained("./merged-model")
# app.py for a Gradio Space
import gradio as gr
from transformers import pipeline
pipe = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
def classify(text):
result = pipe(text)[0]
return f"{result['label']}: {result['score']:.3f}"
demo = gr.Interface(fn=classify, inputs="text", outputs="text", title="Sentiment Classifier")
demo.launch()
requirements.txt:
transformers>=4.40.0
torch>=2.2.0
gradio>=4.0.0
from gradio_client import Client
client = Client("hf-audio/whisper-large-v3")
result = client.predict(
audio="path/to/audio.wav",
api_name="/predict",
)
print(result)
from huggingface_hub import HfApi
api = HfApi(token="hf_...")
# Create Space
api.create_repo(
repo_id="username/my-space",
repo_type="space",
space_sdk="gradio",
private=False,
)
# Upload files
api.upload_folder(
folder_path="./my-space-app",
repo_id="username/my-space",
repo_type="space",
)
Verify: Space appears at https://huggingface.co/spaces/username/my-space and status is RUNNING.
| Task | Recommended Models | Notes |
|---|---|---|
| Text generation | meta-llama/Llama-3.1-8B-Instruct, mistralai/Mistral-7B-Instruct-v0.3 | Instruction-tuned for chat |
| Text classification | distilbert-base-uncased-finetuned-sst-2-english | Fast, lightweight |
| Token classification (NER) | dslim/bert-base-NER | Strong NER baseline |
| Question answering | deepset/roberta-base-squad2 | SQuAD2 trained |
| Summarization | facebook/bart-large-cnn | News summarization |
| Translation | Helsinki-NLP/opus-mt-{src}-{tgt} | Replace src/tgt with language codes |
| Embeddings | sentence-transformers/all-MiniLM-L6-v2 | Fast semantic similarity |
| Image classification | google/vit-base-patch16-224 | Vision Transformer baseline |
| Object detection | facebook/detr-resnet-50 | COCO-trained |
| Image generation | stabilityai/stable-diffusion-xl-base-1.0 | SDXL for high quality |
| ASR (speech-to-text) | openai/whisper-large-v3 | Best accuracy |
| Text-to-speech | suno/bark | Expressive TTS |
| Multimodal (vision+language) | Qwen/Qwen2-VL-7B-Instruct, llava-hf/llava-1.5-7b-hf | VQA and captioning |
pipeline_tag — must match intended taskapache-2.0 or mit for commercial use; llama license has restrictions# CLI: search by task
huggingface-cli repo search --filter pipeline_tag:text-generation --limit 10
Before fine-tuning, evaluate whether prompting alone solves your problem. Fine-tuning is justified for: domain-specific knowledge injection, controlled output style, hallucination reduction in narrow domains, and specialized task optimization at scale.
Evaluate fine-tuned models in production-like conditions:
# Serve the fine-tuned model with TGI or vLLM for realistic latency testing
docker run --gpus all -p 8080:80 \
ghcr.io/huggingface/text-generation-inference:latest \
--model-id username/my-finetuned-model
# Run evaluation harness against the served model
lm_eval --model openai-chat-completions \
--model_args base_url=http://localhost:8080/v1,model=username/my-finetuned-model \
--tasks mmlu,hellaswag \
--output_path ./eval-results/
Verify: Perplexity on held-out validation set decreases. Task-specific benchmark scores match or exceed baseline model.
HF_TOKEN env var or huggingface-cli logindevice_map="auto" or Inference APIattn_implementation — for supported models, flash_attention_2 gives 2-4x speedupprivate=True or use push_to_hub(private=True)pipeline() in production fine-tuning loops — use AutoModel + Trainer for controlmodel.generate() without max_new_tokens — unbounded generation hangspacking=True in SFTConfig dramatically reduces training time by filling context windowsfp16=True when bf16 is available — bfloat16 is more numerically stable for LLM fine-tuning on Ampere+ GPUs# Install core stack
pip install transformers datasets peft trl accelerate bitsandbytes
# Optional: flash attention (Linux + CUDA only)
pip install flash-attn --no-build-isolation
# Login to Hub
huggingface-cli login # paste HF_TOKEN when prompted
# Verify GPU
python -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0))"
python-backend-expert — Python project setup and best practicesdebugging — Systematic debugging for training instabilitiesmcp-catalog — MCP server selection and configurationBefore starting any Hugging Face task, search for existing model loading code and dataset pipelines:
pnpm search:code "from transformers OR from datasets OR InferenceClient OR SFTTrainer"
pnpm search:code "huggingface fine-tuning"
Use Skill({ skill: 'ripgrep' }) to find existing .py training scripts. Use Skill({ skill: 'code-semantic-search' }) to find similar ML pipeline patterns by intent.
Before starting any task, you must query semantic memory and read recent static memory:
node .claude/lib/memory/memory-search.cjs "huggingface transformers fine-tuning model selection"
Read .claude/context/memory/learnings.md
Read .claude/context/memory/decisions.md
Check for prior model selections, known HF API rate limits, tokenizer issues, and CUDA compatibility gotchas.
After completing work, record findings:
.claude/context/memory/decisions.md.claude/context/memory/issues.md.claude/context/memory/learnings.mdDuring long tasks: Use .claude/context/memory/active_context.md as scratchpad.
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.