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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("transformers-community/contrastive-search")
model = AutoModelForCausalLM.from_pretrained("transformers-community/contrastive-search", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

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
```

cyrilvallez HF Staff commited on Mar 18

Commit

cd66a74

verified ·

1 Parent(s): 2ffb799

Fix for new versions

Browse files

Files changed (1) hide show

custom_generate/generate.py +3 -1

custom_generate/generate.py CHANGED Viewed

@@ -181,7 +181,9 @@ def _contrastive_search(
     # keep track of which sequences are already finished
     batch_size, cur_len = input_ids.shape[:2]
     unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
-    model_kwargs = model._get_initial_cache_position(cur_len, input_ids.device, model_kwargs)
     # Create cosine_matrix_mask based on the attention_mask
     cosine_matrix_mask = torch.ones_like(input_ids, dtype=torch.long)

     # keep track of which sequences are already finished
     batch_size, cur_len = input_ids.shape[:2]
     unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
+    # Does not exist anymore in recent versions!
+    if hasattr(model, "_get_initial_cache_position"):
+        model_kwargs = model._get_initial_cache_position(cur_length, input_ids.device, model_kwargs)
     # Create cosine_matrix_mask based on the attention_mask
     cosine_matrix_mask = torch.ones_like(input_ids, dtype=torch.long)