# Picking the Right Model Family — Hugging Face Transformers

Source: https://www.skillbyai.com/en/hugging-face/hf-model-families

> Match encoder, decoder and encoder-decoder checkpoints to tasks, and compute sentence embeddings.

## Which family for which job

Choosing a model starts with the task. **Encoder-only** models (BERT, RoBERTa, DeBERTa and smaller distilled versions) read the whole input bidirectionally and are strong, cheap choices for classification, NER, extractive QA and **embeddings** for semantic search. **Decoder-only** models (GPT-2, Llama, Mistral, Qwen, Gemma and similar) predict the next token and are loaded with `AutoModelForCausalLM`; they power chatbots, code assistants and open-ended generation. **Encoder-decoder** models (T5, BART, many translation models) are loaded with `AutoModelForSeq2SeqLM` and map one sequence to another. A large decoder can also classify by prompting, but a small fine-tuned encoder is often faster, cheaper and more consistent for a fixed label set. To get a sentence embedding from an encoder, a common recipe is **mean pooling**: average the token vectors, ignoring padding via the attention mask. Dedicated embedding models trained for similarity usually beat raw BERT vectors.

## Mean-pooled sentence embeddings

Multiply by the mask so padding vectors do not drag the average, then compare with cosine similarity.

```python
import torch
from transformers import AutoTokenizer, AutoModel

model_id = "sentence-transformers/all-MiniLM-L6-v2"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id)

sentences = ["How do I reset my password?", "I forgot my login password"]
inputs = tok(sentences, padding=True, truncation=True, return_tensors="pt")
with torch.inference_mode():
    hidden = model(**inputs).last_hidden_state          # (batch, tokens, dim)

mask = inputs["attention_mask"].unsqueeze(-1).float()  # (batch, tokens, 1)
emb = (hidden * mask).sum(dim=1) / mask.sum(dim=1)     # mean over real tokens
emb = torch.nn.functional.normalize(emb, dim=-1)
print(float(emb[0] @ emb[1]))  # cosine similarity, high for paraphrases
```

## Reader, writer, translator

An encoder is the student who reads the whole question paper before answering. A decoder is the storyteller who speaks one word at a time and cannot take words back. An encoder-decoder is the court interpreter who listens to the full sentence first, then speaks it again in another language.

**Quiz:** Which Auto class should you use to load a decoder-only chat model for text generation?

- [ ] AutoModelForSeq2SeqLM
- [x] AutoModelForCausalLM
- [ ] AutoModelForMaskedLM
- [ ] AutoModelForSequenceClassification

*Answer:* AutoModelForCausalLM. Decoder-only models are causal language models. Seq2SeqLM is for encoder-decoder models such as T5 and BART.