transformers
GitHub用于使用Hugging Face Transformers库进行NLP、CV、音频及多模态任务。支持模型加载、推理、微调，涵盖文本生成、分类、问答、翻译等场景，提供Pipeline快速接口及高级训练管理功能。
Trigger Scenarios
Install
npx skills add synthetic-sciences/openscience --skill transformers -g -y
SKILL.md
Frontmatter
{
"name": "transformers",
"tags": [
"NLP",
"Deep Learning",
"Hugging Face",
"LLM",
"Fine-Tuning"
],
"author": "Synthetic Sciences",
"license": "Apache-2.0 license",
"version": "1.0.0",
"category": "llm-tools",
"metadata": {
"skill-author": "Synthetic Sciences"
},
"description": "This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.",
"dependencies": [
"transformers>=4.45.0",
"torch>=2.0.0",
"tokenizers>=0.19.0"
],
"compatibility": "Some features require an Huggingface token"
}
Transformers
Overview
The Hugging Face Transformers library provides access to thousands of pre-trained models for tasks across NLP, computer vision, audio, and multimodal domains. Use this skill to load models, perform inference, and fine-tune on custom data.
Installation
Install transformers and core dependencies:
uv pip install torch transformers datasets evaluate accelerate
For vision tasks, add:
uv pip install timm pillow
For audio tasks, add:
uv pip install librosa soundfile
Credential Setup
HuggingFace token is auto-injected by openscience when connected via the dashboard.
# Verify credentials
[ -n "$HF_TOKEN" ] && echo "HF_TOKEN set" || echo "NOT SET"
If not set: connect HuggingFace at https://app.syntheticsciences.ai -> Services, then restart openscience.
Quick Start
Use the Pipeline API for fast inference without manual configuration:
from transformers import pipeline
# Text generation
generator = pipeline("text-generation", model="gpt2")
result = generator("The future of AI is", max_length=50)
# Text classification
classifier = pipeline("text-classification")
result = classifier("This movie was excellent!")
# Question answering
qa = pipeline("question-answering")
result = qa(question="What is AI?", context="AI is artificial intelligence...")
Core Capabilities
1. Pipelines for Quick Inference
Use for simple, optimized inference across many tasks. Supports text generation, classification, NER, question answering, summarization, translation, image classification, object detection, audio classification, and more.
When to use: Quick prototyping, simple inference tasks, no custom preprocessing needed.
See references/pipelines.md for comprehensive task coverage and optimization.
2. Model Loading and Management
Load pre-trained models with fine-grained control over configuration, device placement, and precision.
When to use: Custom model initialization, advanced device management, model inspection.
See references/models.md for loading patterns and best practices.
3. Text Generation
Generate text with LLMs using various decoding strategies (greedy, beam search, sampling) and control parameters (temperature, top-k, top-p).
When to use: Creative text generation, code generation, conversational AI, text completion.
See references/generation.md for generation strategies and parameters.
4. Training and Fine-Tuning
Fine-tune pre-trained models on custom datasets using the Trainer API with automatic mixed precision, distributed training, and logging.
When to use: Task-specific model adaptation, domain adaptation, improving model performance.
See references/training.md for training workflows and best practices.
5. Tokenization
Convert text to tokens and token IDs for model input, with padding, truncation, and special token handling.
When to use: Custom preprocessing pipelines, understanding model inputs, batch processing.
See references/tokenizers.md for tokenization details.
Common Patterns
Pattern 1: Simple Inference
For straightforward tasks, use pipelines:
pipe = pipeline("task-name", model="model-id")
output = pipe(input_data)
Pattern 2: Custom Model Usage
For advanced control, load model and tokenizer separately:
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("model-id")
model = AutoModelForCausalLM.from_pretrained("model-id", device_map="auto")
inputs = tokenizer("text", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
result = tokenizer.decode(outputs[0])
Pattern 3: Fine-Tuning
For task adaptation, use Trainer:
from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=8,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
)
trainer.train()
Reference Documentation
For detailed information on specific components:
- Pipelines:
references/pipelines.md- All supported tasks and optimization - Models:
references/models.md- Loading, saving, and configuration - Generation:
references/generation.md- Text generation strategies and parameters - Training:
references/training.md- Fine-tuning with Trainer API - Tokenizers:
references/tokenizers.md- Tokenization and preprocessing
Version History
- e9844a4 Current 2026-07-11 17:27
Dependencies
-
required
transformers>=4.45.0 -
required
torch>=2.0.0 -
required
tokenizers>=0.19.0
