Update README.md

README.md

@@ -24,5 +24,49 @@ Training recipe:
 
 - GitHub: https://github.com/apple/ml-diffucoder
 
+```
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+model_path = "apple/DiffuCoder-7B-cpGRPO"
+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()
+
+query = "Write a function to find the shared elements from the given two lists."
+prompt = f"""<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+{query.strip()}
+<|im_end|>
+<|im_start|>assistant
+""" ## following the template of qwen; you can also use apply_chat_template function
+
+TOKEN_PER_STEP = 1 # diffusion timesteps * TOKEN_PER_STEP = total new tokens
+
+inputs = tokenizer(prompt, return_tensors="pt")
+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=256,
+    output_history=True,
+    return_dict_in_generate=True,
+    steps=256//TOKEN_PER_STEP,
+    temperature=0.4,
+    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('<|dlm_pad|>')[0])
+```
+
 #### Acknowledgement
 To power this HuggingFace model release, we reuse [Dream](https://huggingface.co/Dream-org/Dream-v0-Base-7B)'s modeling architecture and generation utils.