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README.md

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+---
+license: apache-2.0
+language:
+- en
+- zh
+base_model: Qwen/Qwen3-8B
+pipeline_tag: text-generation
+tags:
+- language model
+- parallel-decoding
+---
+
+# WeDLM-8B
+
+**WeDLM-8B** is a diffusion language model that performs parallel decoding under standard causal attention, initialized from [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
+
+This is the **base (pretrained)** version. For the instruction-tuned version, see [WeDLM-8B-Instruct](https://huggingface.co/tencent/WeDLM-8B-Instruct).
+
+📄 Paper (Coming Soon) | 🌐 [Project Page](https://wedlm.github.io) | 💻 [GitHub](https://github.com/tencent/WeDLM)
+
+## Model Details
+
+| Attribute | Value |
+|:----------|:------|
+| Initialized From | [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) |
+| Parameters | 8B |
+| Context Length | 32,768 |
+
+## Quick Start (Recommended)
+
+For **fast inference**, use the `wedlm` engine:
+
+```bash
+pip install git+https://github.com/tencent/WeDLM.git
+```
+
+```python
+from wedlm import LLM, SamplingParams
+
+llm = LLM(model="tencent/WeDLM-8B")
+
+prompt = "The theory of relativity states that"
+outputs = llm.generate([prompt], SamplingParams(max_tokens=256))
+
+print(outputs[0]["text"])
+```
+
+## HuggingFace Transformers
+
+For **training** or simple forward passes, you can load via Transformers:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("tencent/WeDLM-8B", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    "tencent/WeDLM-8B", 
+    trust_remote_code=True,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+inputs = tokenizer("The theory of relativity", return_tensors="pt").to(model.device)
+outputs = model(**inputs)
+```
+
+> ⚠️ **Note:** The HuggingFace interface is for training/forward pass convenience. For optimized inference throughput, use the `wedlm` engine above.
+
+## Performance
+
+| Benchmark | Qwen3-8B | WeDLM-8B |
+|:----------|:--------:|:--------:|
+| ARC-C (0-shot) | 92.66 | **92.92** |
+| GSM8K (3-shot) | 85.97 | **90.20** |
+| MATH (4-shot) | 50.80 | **53.60** |
+| HumanEval (4-shot) | 68.90 | **75.00** |
+| MMLU (5-shot) | 74.03 | **75.46** |
+| **Average** | 72.61 | **74.72** |
+
+## Citation (Coming soon)
+
+
+## License
+
+Apache 2.0