Instructions to use albertge/dream-7b-dllm-registers-mix60k-r4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use albertge/dream-7b-dllm-registers-mix60k-r4 with Transformers:

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

pipe = pipeline("text-generation", model="albertge/dream-7b-dllm-registers-mix60k-r4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("albertge/dream-7b-dllm-registers-mix60k-r4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use albertge/dream-7b-dllm-registers-mix60k-r4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "albertge/dream-7b-dllm-registers-mix60k-r4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "albertge/dream-7b-dllm-registers-mix60k-r4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/albertge/dream-7b-dllm-registers-mix60k-r4

SGLang

How to use albertge/dream-7b-dllm-registers-mix60k-r4 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 "albertge/dream-7b-dllm-registers-mix60k-r4" \
    --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": "albertge/dream-7b-dllm-registers-mix60k-r4",
		"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 "albertge/dream-7b-dllm-registers-mix60k-r4" \
        --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": "albertge/dream-7b-dllm-registers-mix60k-r4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use albertge/dream-7b-dllm-registers-mix60k-r4 with Docker Model Runner:
```
docker model run hf.co/albertge/dream-7b-dllm-registers-mix60k-r4
```

Add paper link

by nielsr HF Staff - opened 27 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-3

nielsr

27 days ago

This PR adds a direct link to the paper Register Tokens for Bounded-State Reasoning in Diffusion Language Models in the model card so users can easily find the original publication.

Add paper link5d328773

albertge changed pull request status to merged 27 days ago

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