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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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[Diverse beam search](https://hf.co/papers/1610.02424) is a variant of beam search that produces more diverse output candidates to choose from. This strategy measures the dissimilarity of sequences and a penalty is applied if sequences are too similar. To avoid high computation costs, the number of beams is divided into groups. |
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Enable diverse beam search with the `num_beams`, `num_beam_groups` and `diversity_penalty` parameters (the `num_beams` parameter should be divisible by `num_beam_groups`). |
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This implementation matches the `group_beam_search` functionality present in `transformers<4.56.0`. |
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--- |
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## Base model |
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* [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) |
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--- |
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## Model compatibility |
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* Decoder-only transformer models |
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--- |
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## Additional Arguments |
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* **`num_beams`** (*int*, optional, defaults to `1`): |
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Number of beams for beam search. If not greater than `num_beam_groups`, will be set to `num_beam_groups`. |
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* **`num_beam_groups`** (*int*, optional, defaults to `1`): |
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Number of groups to divide `num_beams` into for beam search. |
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* **`diversity_penalty`** (*float*, optional, defaults to `0.0`): |
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Diversity penalty applied to beams. |
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* **`early_stopping`** (*bool* or *str*, optional, defaults to `False`): |
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Whether to stop beam search when at least `num_beams` complete candidates are finished per batch or not. If not `False`, it should be an integer greater than 1 indicating the minimum number of beams required to be finished per batch. |
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* **`max_length`** (*int*, optional, defaults to `20`): |
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The maximum length of the generated sequence. |
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* **`num_return_sequences`** (*int*, optional, defaults to `1`): |
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The number of sequences to return. |
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* **`repetition_penalty`** (*float*, optional, defaults to `None`): |
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Helps reduce repetition. A value of `1.2` is recommended. |
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--- |
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## Output Type changes |
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* The `generate` method output remains the same as default `transformers` generation, |
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but logits are post-processed using the DoLa contrastive scoring before token selection. |
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--- |
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## Example usage |
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```py |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device |
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device = infer_device() |
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B") |
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inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt").to(device) |
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model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B", dtype=torch.float16).to(device) |
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# explicitly set to 100 because Llama2 generation length is 4096 |
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outputs = model.generate(**inputs, max_new_tokens=50, num_beams=6, num_beam_groups=3, diversity_penalty=1.0, do_sample=False, custom_generate="transformers-community/group-beam-search", trust_remote_code=True) |
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tokenizer.batch_decode(outputs, skip_special_tokens=True) |
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``` |
