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)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("transformers-community/dola")
model = AutoModelForCausalLM.from_pretrained("transformers-community/dola")
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/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

5f8a24f

verified ·

1 Parent(s): 966b7cb

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README.md +10 -6

README.md CHANGED Viewed

@@ -75,14 +75,16 @@ This implementation matches the `DoLa` functionality present in `transformers<4.
 ```python
 # requires `transformers>=4.56.0`, previously, it was part of the library
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
 model = AutoModelForCausalLM.from_pretrained(
     "Qwen/Qwen3-0.6B", torch_dtype=torch.float16
-).to("cuda")
-inputs = tokenizer("What is the highest peak in the world?", return_tensors="pt").to("cuda")
 outputs = model.generate(
     **inputs,
@@ -102,14 +104,16 @@ print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
 ```python
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
 model = AutoModelForCausalLM.from_pretrained(
     "Qwen/Qwen3-0.6B", torch_dtype=torch.float16
-).to("cuda")
-inputs = tokenizer("What is the highest peak in the world?", return_tensors="pt").to("cuda")
 outputs = model.generate(
     **inputs,

 ```python
 # requires `transformers>=4.56.0`, previously, it was part of the library
 import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device
+device = infer_device()
 tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
 model = AutoModelForCausalLM.from_pretrained(
     "Qwen/Qwen3-0.6B", torch_dtype=torch.float16
+).to(device)
+inputs = tokenizer("What is the highest peak in the world?", return_tensors="pt").to(device)
 outputs = model.generate(
     **inputs,
 ```python
 import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device
+device = infer_device()
 tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
 model = AutoModelForCausalLM.from_pretrained(
     "Qwen/Qwen3-0.6B", torch_dtype=torch.float16
+).to(device)
+inputs = tokenizer("What is the highest peak in the world?", return_tensors="pt").to(device)
 outputs = model.generate(
     **inputs,