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

Libraries

How to use transformers-community/dola with Transformers:

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

pipe = pipeline("text-generation", model="transformers-community/dola")
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/dola")
model = AutoModelForCausalLM.from_pretrained("transformers-community/dola", 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/dola with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/transformers-community/dola

SGLang

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

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

manueldeprada HF Staff commited on Aug 25, 2025

Commit

005232e

verified ·

1 Parent(s): 5f8a24f

Upload folder using huggingface_hub

Browse files

Files changed (1) hide show

custom_generate/generate.py +4 -2

custom_generate/generate.py CHANGED Viewed

@@ -1,7 +1,7 @@
 from typing import Union
 import torch
 from transformers import LogitsProcessorList, StoppingCriteriaList, GenerationConfig
-from transformers.generation.utils import GenerateNonBeamOutput, GenerateDecoderOnlyOutput
 import torch.nn as nn
 import torch.nn.functional as F
 import numpy as np
@@ -332,5 +332,7 @@ def generate(model, *args, **kwargs):
             `'high'` to improve short-answer tasks. Check the [documentation](https://huggingface.co/transformers-community/dola)
             or [the paper](https://huggingface.co/papers/2309.03883) for more details.
     """
-    generation_outputs = model.generate(*args, custom_generate=_dola_decoding, **kwargs)
     return generation_outputs

 from typing import Union
 import torch
 from transformers import LogitsProcessorList, StoppingCriteriaList, GenerationConfig
+from transformers.generation.utils import GenerationMixin, GenerateNonBeamOutput, GenerateDecoderOnlyOutput
 import torch.nn as nn
 import torch.nn.functional as F
 import numpy as np
             `'high'` to improve short-answer tasks. Check the [documentation](https://huggingface.co/transformers-community/dola)
             or [the paper](https://huggingface.co/papers/2309.03883) for more details.
     """
+    generation_outputs = GenerationMixin.generate(
+        model, *args, custom_generate=_dola_decoding, **kwargs
+    )
     return generation_outputs