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transformers-community/
dola

Name: dola
Availability: InStock

text-generation-inference

endpoints_compatible

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Description

Implementation of Decoding by Contrasting Layers (DoLa), a contrastive decoding strategy for improving factuality and reducing hallucinations in language model outputs.

DoLa works by contrasting the logits from the final layer with those from earlier layers of the model, amplifying factual knowledge localized in specific layers and suppressing spurious information.

This can be useful for:

Short-answer tasks (e.g., TruthfulQA) — using higher layers (dola_layers="high")
Long-answer reasoning tasks (e.g., GSM8K, StrategyQA, FACTOR, VicunaQA) — using lower layers (dola_layers="low")

DoLa is not recommended for smaller models such as GPT-2, as the improvement may be negligible.

This implementation matches the DoLa functionality present in transformers<4.53.0.

Base model

Qwen/Qwen3-0.6B

Model compatibility

Decoder-only transformer models

Additional Arguments

dola_layers (str or List[int], optional): Which earlier layers to contrast with the final layer. Can be:
- "low" — lower half of layers (recommended for long answers)
- "high" — upper half of layers (recommended for short answers)
- List of integer indices (e.g., [18, 20])
Note:
- Layer 0 is the word embedding; layer 1 is the first transformer block.
- If the model has tied word embeddings, layer 0 is skipped and counting starts at layer 2.
- Typical defaults:
  
  # Layers "low" range "high" range
  > 40 (0, 20, 2) (N - 20, N, 2)
  ≤ 40 range(0, N//2, 2) range(N//2, N, 2)
repetition_penalty (float, optional, defaults to None): Helps reduce repetition. A value of 1.2 is recommended.

Output Type changes

The generate method output remains the same as default transformers generation, but logits are post-processed using the DoLa contrastive scoring before token selection.

Example usage

Using higher layers (short-answer tasks)

python

# requires `transformers>=4.56.0`, previously, it was part of the library
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device

device = infer_device()

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-0.6B", torch_dtype=torch.float16
).to(device)

inputs = tokenizer("What is the highest peak in the world?", return_tensors="pt").to(device)

outputs = model.generate(
    **inputs,
    max_new_tokens=50,
    do_sample=False,
    custom_generate="transformers-community/dola",
    trust_remote_code=True,
    dola_layers="high"
)

print(tokenizer.batch_decode(outputs, skip_special_tokens=True))

Contrasting specific layers

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device

device = infer_device()

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-0.6B", torch_dtype=torch.float16
).to(device)

inputs = tokenizer("What is the highest peak in the world?", return_tensors="pt").to(device)

outputs = model.generate(
    **inputs,
    max_new_tokens=50,
    do_sample=False,
    repetition_penalty=1.2,
    custom_generate="transformers-community/dola",
    trust_remote_code=True,
    dola_layers=[18, 20]
)

# Only decode the newly generated tokens
print(tokenizer.batch_decode(outputs[:, inputs.input_ids.shape[-1]:], skip_special_tokens=True))

# Layers	`"low"` range	`"high"` range
> 40	`(0, 20, 2)`	`(N - 20, N, 2)`
≤ 40	`range(0, N//2, 2)`	`range(N//2, N, 2)`