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:
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.