Description
Implementation of Contrastive Search, a decoding strategy that jointly optimizes model confidence and a degeneration penalty to produce fluent, coherent, and low-repetition text. At each step, the model considers the top-k candidate tokens and selects the one maximizing:
score(v) = (1 - alpha) * p(v | context) - alpha * max_cosine_similarity(h_v, H_context)
where alpha is the trade-off between confidence and the cosine-similarity-based penalty.
This strategy typically:
- Reduces repetition compared to greedy/beam search
- Preserves semantic coherence better than pure sampling
Base model
Qwen/Qwen2.5-0.5B-Instruct(example)
Model compatibility
- Decoder and encoder-decoder transformer models for causal LM
Additional Arguments
top_k(int): Number of candidate tokens to consider each step (e.g., 4)penalty_alpha(float): Weight of the degeneration penalty (e.g., 0.6)
Tips:
- Larger
top_kexplores more candidates but increases compute penalty_alphain [0.3, 0.8] often works well;0.0reduces to greedy
Output Type changes
(none) — returns the same structure as standard transformers generation
Example usage
from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device
device = infer_device()
model_id = "Qwen/Qwen2.5-0.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto").to(device)
inputs = tokenizer(["DeepMind Company is"], return_tensors="pt").to(device)
# Contrastive search
gen_out = model.generate(
**inputs,
custom_generate="contrastive_search",
penalty_alpha=0.6,
top_k=4,
max_new_tokens=128,
trust_remote_code=True,
)
print(tokenizer.batch_decode(gen_out, skip_special_tokens=True))