Alterlab Transformers

Transformers

Overview

Hugging Face Transformers loads pretrained checkpoints from the Hub for NLP, vision, audio, and multimodal tasks, runs inference through pipeline() or the Auto* classes, and fine-tunes them with Trainer. This skill targets transformers v5 (≥ 5.0; current 5.17.0 as of 2026-09). v5 is PyTorch-only and changes several v4 idioms, so check "v5 changes that break v4 code" below before reusing older snippets from papers, blogs, or model cards.

When to Use This Skill

Use this skill when the user wants to:

• Run quick inference with a Hub checkpoint via pipeline() — text classification, NER, zero-shot classification, text generation with a chat model, speech recognition, image classification/detection, or image-text-to-text with a VLM.
• Load a model plus tokenizer/processor with explicit dtype, device_map, attention backend, or 4/8-bit quantization.
• Control decoding in model.generate() (greedy, sampling, beam search, streaming, chat templates).
• Fine-tune an encoder or decoder on a custom labelled dataset with Trainer, optionally with LoRA via peft.
• Port v4-era code (torch_dtype=, load_in_8bit=, removed pipelines, TF/Flax models) to v5.

Does NOT Trigger

Installation

bashCopy code
uv pip install "transformers>=5" torch accelerate datasets

• Vision: add pillow and torchvision (the default image-processor backend; timm only for timm-backed models).
• Audio: add librosa soundfile.
• LoRA / quantization: peft, bitsandbytes (CUDA). Metrics: evaluate or plain scikit-learn metrics.

Authentication

Gated or private models need a Hub token. Either log in once:

pythonCopy code
from huggingface_hub import login
login()  # or run `hf auth login` in a shell (huggingface-cli is deprecated)

or export the variable the Hub client reads:

bashCopy code
export HF_TOKEN="your_token_here"

Tokens: https://huggingface.co/settings/tokens. Pass token= (not the removed use_auth_token=) when you need it explicitly.

Quick Start

pythonCopy code
from transformers import pipeline

# Text generation with a small open chat model (CPU-friendly; add device_map="auto" on GPU)
generator = pipeline("text-generation", model="Qwen/Qwen3-0.6B")
messages = [{"role": "user", "content": "Explain p-hacking in two sentences."}]
out = generator(
    messages,
    max_new_tokens=128,
    tokenizer_encode_kwargs={"enable_thinking": False},  # Qwen3 chat-template switch; omit for other models
)
print(out[0]["generated_text"][-1]["content"])

# Text classification — pin the checkpoint; task defaults change between releases
classifier = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
classifier(["This movie was excellent!", "Terrible pacing."])

# Zero-shot classification
zero_shot = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
zero_shot("The grant covers two postdoc salaries.", candidate_labels=["funding", "teaching", "ethics"])

Use max_new_tokens (tokens to generate) rather than max_length (prompt + output, and in the text-generation pipeline it also feeds tokenizer truncation). Summarization, translation, and question answering no longer have dedicated pipelines in v5 — prompt a chat model as above.

v5 Changes That Break v4 Code

Source: the official v5 migration guide, cross-checked against the 5.17.0 release.

Core Capabilities

1. Pipelines for Quick Inference

One call covers tokenization, the forward pass, and post-processing for text classification, NER, zero-shot, fill-mask, text generation, ASR, audio classification, image classification/segmentation, object detection, depth estimation, and image-text-to-text. Use for prototyping and batch inference without custom preprocessing. See references/pipelines.md.

2. Model Loading and Management

from_pretrained with dtype, device_map, attn_implementation (SDPA by default), and quantization_config; saving, Hub upload, and ONNX export options. See references/models.md.

3. Text Generation

generate() with greedy, sampling (temperature/top-k/top-p), and beam search; chat templates, streaming, and static caches. See references/generation.md.

4. Training and Fine-Tuning

Trainer + TrainingArguments with mixed precision, gradient accumulation/checkpointing, callbacks, hyperparameter search, and LoRA via peft. See references/training.md.

5. Tokenization

Padding, truncation, special tokens, offsets, and chat templates. See references/tokenizers.md.

Common Patterns

Pattern 1: Simple Inference

pythonCopy code
pipe = pipeline("task-name", model="org/model-id")
output = pipe(input_data)

Pattern 2: Explicit Model + Tokenizer (chat model)

pythonCopy code
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Qwen/Qwen3-0.6B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto", device_map="auto")

messages = [{"role": "user", "content": "State the central limit theorem in one sentence."}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    enable_thinking=False,  # Qwen3-specific template variable
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
new_tokens = outputs[0, inputs["input_ids"].shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))

Pattern 3: Fine-Tuning a Classifier

pythonCopy code
from datasets import load_dataset
from transformers import (AutoModelForSequenceClassification, AutoTokenizer,
                          DataCollatorWithPadding, Trainer, TrainingArguments)

model_id = "google-bert/bert-base-uncased"  # or answerdotai/ModernBERT-base
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=5)

ds = load_dataset("Yelp/yelp_review_full")
ds = ds.map(lambda b: tokenizer(b["text"], truncation=True), batched=True)

args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=16,
    eval_strategy="epoch",
    warmup_steps=0.1,          # ratio of total steps
    report_to="tensorboard",   # default is "none" in v5
)
trainer = Trainer(
    model=model,
    args=args,
    train_dataset=ds["train"],
    eval_dataset=ds["test"],
    processing_class=tokenizer,
    data_collator=DataCollatorWithPadding(tokenizer),
)
trainer.train()

Practical Notes

• Pin model IDs (and ideally revision=) in research code: pipeline defaults and Hub repos change, which silently changes results.
• Report the exact checkpoint, transformers version, decoding parameters, and seed (transformers.set_seed) when generated text or fine-tuned metrics appear in a paper.
• Check the model card's license and gating terms before redistributing weights or outputs.

Reference Documentation

• references/pipelines.md — supported v5 tasks, parameters, batching, and removed pipelines
• references/models.md — loading, dtype/device/attention/quantization, saving, export
• references/generation.md — decoding strategies, chat templates, streaming, caches
• references/training.md — Trainer workflow, TrainingArguments, PEFT, tuning
• references/tokenizers.md — tokenization, special tokens, chat templates

Part of the AlterLab Academic Skills suite.