Instructions to use transformers-community/group-beam-search with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use transformers-community/group-beam-search with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="transformers-community/group-beam-search")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("transformers-community/group-beam-search")
model = AutoModelForCausalLM.from_pretrained("transformers-community/group-beam-search")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use transformers-community/group-beam-search with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "transformers-community/group-beam-search"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "transformers-community/group-beam-search",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/transformers-community/group-beam-search

SGLang

How to use transformers-community/group-beam-search with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "transformers-community/group-beam-search" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "transformers-community/group-beam-search",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "transformers-community/group-beam-search" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "transformers-community/group-beam-search",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use transformers-community/group-beam-search with Docker Model Runner:
```
docker model run hf.co/transformers-community/group-beam-search
```

Description

Diverse beam search 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.

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).

This implementation matches the group_beam_search functionality present in transformers<4.56.0.

Base model

Qwen/Qwen3-0.6B

Model compatibility

Decoder-only transformer models

Additional Arguments

num_beams (int, optional, defaults to 1): Number of beams for beam search. If not greater than num_beam_groups, will be set to num_beam_groups.
num_beam_groups (int, optional, defaults to 1): Number of groups to divide num_beams into for beam search.
diversity_penalty (float, optional, defaults to 0.0): Diversity penalty applied to beams.
early_stopping (bool or str, optional, defaults to False): 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.
max_length (int, optional, defaults to 20): The maximum length of the generated sequence.
num_return_sequences (int, optional, defaults to 1): The number of sequences to return.
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

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device

device = infer_device()

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt").to(device)

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B", dtype=torch.float16).to(device)
# explicitly set to 100 because Llama2 generation length is 4096
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)
tokenizer.batch_decode(outputs, skip_special_tokens=True)

Downloads last month: 21

Safetensors

Model size

0.8B params

Tensor type

BF16

Paper for transformers-community/group-beam-search

Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models

Paper • 1610.02424 • Published Oct 7, 2016 • 1