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