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--- |
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base_model: |
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- apple/DiffuCoder-7B-Base |
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tags: |
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- code |
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- text-diffusion-model |
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- diffusion large language model |
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license: unknown |
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--- |
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### DiffuCoder-7B-Instruct |
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The DiffuCoder-7B-Instruct model builds on the DiffuCoder-7B-Base checkpoint with instruction-tuning to better follow code-related prompts. |
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- Training recipe: with a newly introduced pad token, we train this model with fixed length conditionally on [OpenCoder-SFT](https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage2) data for 5 epochs. |
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- Benchmarks: Demonstrates stronger instruction-following capabilities than the Base model. |
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#### More details and usage examples: |
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- Paper: [DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation](https://arxiv.org/abs/2506.20639) |
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- GitHub: https://github.com/apple/ml-diffucoder |
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``` |
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import torch |
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from transformers import AutoModel, AutoTokenizer |
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model_path = "apple/DiffuCoder-7B-Instruct" |
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model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16, trust_remote_code=True) |
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) |
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model = model.to("cuda").eval() |
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query = "Write a function to find the shared elements from the given two lists." |
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prompt = f"""<|im_start|>system |
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You are a helpful assistant.<|im_end|> |
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<|im_start|>user |
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{query.strip()} |
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<|im_end|> |
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<|im_start|>assistant |
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""" ## following the template of qwen; you can also use apply_chat_template function |
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TOKEN_PER_STEP = 1 # diffusion timesteps * TOKEN_PER_STEP = total new tokens |
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inputs = tokenizer(prompt, return_tensors="pt") |
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input_ids = inputs.input_ids.to(device="cuda") |
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attention_mask = inputs.attention_mask.to(device="cuda") |
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output = model.diffusion_generate( |
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input_ids, |
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attention_mask=attention_mask, |
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max_new_tokens=256, |
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output_history=True, |
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return_dict_in_generate=True, |
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steps=256//TOKEN_PER_STEP, |
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temperature=0.3, |
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top_p=0.95, |
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alg="entropy", |
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alg_temp=0., |
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) |
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generations = [ |
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tokenizer.decode(g[len(p) :].tolist()) |
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for p, g in zip(input_ids, output.sequences) |
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] |
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print(generations[0].split('<|dlm_pad|>')[0]) |
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``` |
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#### Acknowledgement |
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To power this HuggingFace model release, we reuse [Dream](https://huggingface.co/Dream-org/Dream-v0-Base-7B)'s modeling architecture and generation utils. |
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