Create README.md

README.md

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+---
+library_name: transformers
+license: apache-2.0
+---
+# 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])
+```