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

Libraries

How to use microsoft/DialoGPT-medium with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForMultimodalLM.from_pretrained("microsoft/DialoGPT-medium")
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-medium with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

New architecture: TemporalMesh Transformer — dynamic kNN graph attention + per-token exit routing, 29.4 PPL at 48% compute

#32

by vigneshwar234 - opened 11 days ago

Discussion

vigneshwar234

11 days ago

TemporalMesh Transformer (TMT) — open-source, 120M params, state-of-the-art efficiency

TMT achieves 29.4 PPL on WikiText-2 (−30.2% vs vanilla) at 48% relative compute — outperforming Mamba (31.8), RWKV (33.1), and vanilla transformer (42.1) at ~120M parameters.

5 innovations in one forward pass: Mesh Attention (dynamic kNN graph, O(S·k)), Temporal Decay Encoding (learned multiplicative post-softmax), Adaptive Depth Routing (per-token exit gate, 52% compute saved), Dual-Stream FFN, EMA Memory Anchors.

	WT-2 PPL↓	LongBench↑	C4 PPL↓	Compute
Vanilla	42.1	41.2	38.4	100%
Mamba	31.8	51.3	30.1	55%
TMT	29.4	53.4	27.4	48%

📄 https://zenodo.org/records/20287390 · 💻 https://github.com/vignesh2027/TemporalMesh-Transformer · 🎮 https://huggingface.co/spaces/vigneshwar234/TemporalMesh-Transformer-Demo

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