Instructions to use bdbj/Dream-Coder-v0-Instruct-7B-SM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bdbj/Dream-Coder-v0-Instruct-7B-SM with Transformers:

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

pipe = pipeline("text-generation", model="bdbj/Dream-Coder-v0-Instruct-7B-SM", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bdbj/Dream-Coder-v0-Instruct-7B-SM", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bdbj/Dream-Coder-v0-Instruct-7B-SM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bdbj/Dream-Coder-v0-Instruct-7B-SM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bdbj/Dream-Coder-v0-Instruct-7B-SM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bdbj/Dream-Coder-v0-Instruct-7B-SM

SGLang

How to use bdbj/Dream-Coder-v0-Instruct-7B-SM 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 "bdbj/Dream-Coder-v0-Instruct-7B-SM" \
    --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": "bdbj/Dream-Coder-v0-Instruct-7B-SM",
		"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 "bdbj/Dream-Coder-v0-Instruct-7B-SM" \
        --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": "bdbj/Dream-Coder-v0-Instruct-7B-SM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bdbj/Dream-Coder-v0-Instruct-7B-SM with Docker Model Runner:
```
docker model run hf.co/bdbj/Dream-Coder-v0-Instruct-7B-SM
```

Dream-Coder-v0-Instruct-7B-SM / README.md

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	---
	library_name: transformers
	license: apache-2.0
	pipeline_tag: text-generation
	---
	# Dream-Coder-v0-Instruct-7B

	Dream-Coder 7B is a diffusion LLM for code trained exclusively on open-source data across its development stages—adaptation, supervised fine-tuning, and reinforcement learning.
	It achieves an impressive 21.4% pass@1 on LiveCodeBench (2410-2505), outperforming other open-source diffusion LLMs by a wide margin.
	More details about the model and usage can be found in the blog and github bellow:

	- Blog: https://hkunlp.github.io/blog/2025/dream-coder/
	- Github: https://github.com/DreamLM/Dream-Coder

	## Quickstart
	To get start with,
	please install `transformers==4.46.2` and `torch==2.5.1`. Here is an example to use Dream-Coder 7B:

	```python
	import torch
	from transformers import AutoModel, AutoTokenizer

	model_path = "Dream-org/Dream-Coder-v0-Instruct-7B"
	model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16, trust_remote_code=True)
	tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
	model = model.to("cuda").eval()

	messages = [
	{"role": "user", "content": "Write a quick sort algorithm."}
	]
	inputs = tokenizer.apply_chat_template(
	messages, return_tensors="pt", return_dict=True, add_generation_prompt=True
	)
	input_ids = inputs.input_ids.to(device="cuda")
	attention_mask = inputs.attention_mask.to(device="cuda")

	output = model.diffusion_generate(
	input_ids,
	attention_mask=attention_mask,
	max_new_tokens=768,
	output_history=True,
	return_dict_in_generate=True,
	steps=768,
	temperature=0.1,
	top_p=0.95,
	alg="entropy",
	alg_temp=0.,
	)
	generations = [
	tokenizer.decode(g[len(p) :].tolist())
	for p, g in zip(input_ids, output.sequences)
	]

	print(generations[0].split(tokenizer.eos_token)[0])
	```

	We adapt the original Dream-7B-Coder-Instruct with soft-masking (SM). More information on SM will be linked here in the future.