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