Instructions to use nvidia/Efficient-DLM-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/Efficient-DLM-4B with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/Efficient-DLM-4B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("nvidia/Efficient-DLM-4B", trust_remote_code=True)
model = AutoModel.from_pretrained("nvidia/Efficient-DLM-4B", trust_remote_code=True)
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 nvidia/Efficient-DLM-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Efficient-DLM-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Efficient-DLM-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/Efficient-DLM-4B

SGLang

How to use nvidia/Efficient-DLM-4B 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 "nvidia/Efficient-DLM-4B" \
    --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": "nvidia/Efficient-DLM-4B",
		"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 "nvidia/Efficient-DLM-4B" \
        --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": "nvidia/Efficient-DLM-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/Efficient-DLM-4B with Docker Model Runner:
```
docker model run hf.co/nvidia/Efficient-DLM-4B
```

Efficient-DLM-4B / README.md

YongganFu

Update README.md

a51d086 verified 7 months ago

1.4 kB

library_name: transformers
tags: []

Nemotron-Diffusion-Research-4B-v0

Developed by DLER team @ NVR and will be updated actively. Contact Yonggan Fu and Pavlo Molchanov for any question.

Environment

Docker path: /lustre/fsw/portfolios/nvr/users/yongganf/docker/megatron_py25_dllm.sqsh on OCI-ORD/OCI-NRT. Apply for interactive nodes with the following command:

srun -A {account} --partition interactive --time 4:00:00 --gpus 8 --container-image /lustre/fsw/portfolios/nvr/users/yongganf/docker/megatron_py25_dllm.sqsh --container-mounts=$HOME:/home,/lustre:/lustre  --pty bash

Chat with Our Model

from transformers import AutoModel, AutoTokenizer
import torch

repo_name = "nvidia/Nemotron-Diffusion-Research-4B-v0"

tokenizer = AutoTokenizer.from_pretrained(repo_name, trust_remote_code=True)
model = AutoModel.from_pretrained(repo_name, trust_remote_code=True)
model = model.cuda().to(torch.bfloat16)

user_input = input("User: ").strip()

prompt_ids = tokenizer(user_input,return_tensors='pt').input_ids.to(device='cuda')
out_ids, nfe = model.generate(prompt_ids, max_new_tokens=128, steps=128, block_length=32, shift_logits=False, threshold=0.9)

tokenized_out = tokenizer.batch_decode(out_ids[:, prompt_ids.shape[1]:], skip_special_tokens=True)[0]
print(f"Model: {tokenized_out}")
print(f"[Num Function Eval (NFE)={nfe}]")