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

Libraries

How to use transformers-community/contrastive-search with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("transformers-community/contrastive-search")
model = AutoModelForCausalLM.from_pretrained("transformers-community/contrastive-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 Settings

vLLM

How to use transformers-community/contrastive-search with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "transformers-community/contrastive-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/contrastive-search",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/transformers-community/contrastive-search

SGLang

How to use transformers-community/contrastive-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/contrastive-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/contrastive-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/contrastive-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/contrastive-search",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

allow Output subclasses in contrastive search

by jood-canva - opened Oct 22, 2025

base: refs/heads/main

←

from: refs/pr/4

Discussion Files changed

-3

Files changed (1) hide show

custom_generate/generate.py +6 -3

custom_generate/generate.py CHANGED Viewed

@@ -1,3 +1,4 @@
 import logging
 from typing import TYPE_CHECKING, Optional, Union
@@ -14,7 +15,6 @@ from transformers.generation.utils import (
     GenerateNonBeamOutput,
     GenerationMixin,
 )
-from transformers.modeling_outputs import CausalLMOutputWithPast, Seq2SeqLMOutput
 from transformers.utils import ModelOutput
@@ -414,7 +414,8 @@ def _contrastive_search(
                 for layer in outputs.decoder_attentions:
                     layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
                     next_step_decoder_attentions += (layer,)
-            outputs = Seq2SeqLMOutput(
                 past_key_values=next_past_key_values,
                 decoder_hidden_states=next_decoder_hidden_states,
                 decoder_attentions=next_step_decoder_attentions or None,
@@ -426,11 +427,13 @@ def _contrastive_search(
                 for layer in outputs.attentions:
                     layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
                     next_step_attentions += (layer,)
-            outputs = CausalLMOutputWithPast(
                 past_key_values=next_past_key_values,
                 hidden_states=next_decoder_hidden_states,
                 attentions=next_step_attentions or None,
             )
         # contrastive_search main logic end
         # synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping

+from dataclasses import replace
 import logging
 from typing import TYPE_CHECKING, Optional, Union
     GenerateNonBeamOutput,
     GenerationMixin,
 )
 from transformers.utils import ModelOutput
                 for layer in outputs.decoder_attentions:
                     layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
                     next_step_decoder_attentions += (layer,)
+            outputs = replace(
+                outputs,
                 past_key_values=next_past_key_values,
                 decoder_hidden_states=next_decoder_hidden_states,
                 decoder_attentions=next_step_decoder_attentions or None,
                 for layer in outputs.attentions:
                     layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
                     next_step_attentions += (layer,)
+            outputs = replace(
+                outputs,
                 past_key_values=next_past_key_values,
                 hidden_states=next_decoder_hidden_states,
                 attentions=next_step_attentions or None,
             )
         # contrastive_search main logic end
         # synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping