Instructions to use microsoft/DialoGPT-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/DialoGPT-small with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/DialoGPT-small")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/DialoGPT-small with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/microsoft/DialoGPT-small

SGLang

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

Docker Model Runner
How to use microsoft/DialoGPT-small with Docker Model Runner:
```
docker model run hf.co/microsoft/DialoGPT-small
```

DPO Trainer for DialoGPT (Train/loss does not decrease / result is not in downward trend)

#12

by jabsss - opened Jan 12, 2025

Discussion

jabsss

Jan 12, 2025

Hi, our group is currently working on a pest management chatbot. And we are currently testing which model can cater both English and Tagalog.

Right now, we're having problems with our DPO Trainer since the graph is not showing ideal results, specially the train/loss results. I have tweaked our code and parameters for a while now, and the results of the graph for the loss is either linear, increasing, or spiky.

Here is our parameters:

@dataclass
class ScriptArguments:
beta: Optional[float] = field(default=0.1, metadata={"help": "The beta parameter for DPO loss."})
model_name_or_path: Optional[str] = field(default=MULTI_LANG_MODEL, metadata={"help": "Path to the model."})
learning_rate: Optional[float] = field(default=2e-5, metadata={"help": "Optimizer learning rate."})
per_device_train_batch_size: Optional[int] = field(default=16, metadata={"help": "Training batch size per device."})
gradient_accumulation_steps: Optional[int] = field(default=4, metadata={"help": "Gradient accumulation steps."})
warmup_steps: Optional[int] = field(default=1000, metadata={"help": "Learning rate warmup steps."})
max_grad_norm: Optional[float] = field(default=0.1, metadata={"help": "Gradient clipping max norm."})
lora_alpha: Optional[float] = field(default=16, metadata={"help": "Lora alpha for LoRA fine-tuning."})
lora_dropout: Optional[float] = field(default=0.05, metadata={"help": "Lora dropout rate."})
lora_r: Optional[int] = field(default=32, metadata={"help": "Lora R parameter."})
max_prompt_length: Optional[int] = field(default=64, metadata={"help": "Max prompt length for inputs."})
max_length: Optional[int] = field(default=512, metadata={"help": "Max length for model outputs."})
num_train_epochs: Optional[int] = field(default=1, metadata={"help": "Number of training epochs."})
output_dir: Optional[str] = field(default="./dpo_results", metadata={"help": "Directory to save model outputs."})
Datasets = 40k

Can someone please help me with our training? Thank you in advance!

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