Upload folder using huggingface_hub

README.md

@@ -0,0 +1,104 @@
+---
+license: apache-2.0
+language:
+- en
+- zh
+base_model: tencent/WeDLM-7B
+pipeline_tag: text-generation
+tags:
+- language model
+- parallel-decoding
+- chat
+- instruct
+---
+
+# WeDLM-7B-Instruct
+
+**WeDLM-7B-Instruct** is an instruction-tuned diffusion language model that performs parallel decoding under standard causal attention, fine-tuned from [WeDLM-7B](https://huggingface.co/tencent/WeDLM-7B).
+
+For the base (pretrained) version, see [WeDLM-7B](https://huggingface.co/tencent/WeDLM-7B).
+
+📄 Paper (Coming Soon) | 🌐 [Project Page](https://wedlm.github.io) | 💻 [GitHub](https://github.com/tencent/WeDLM)
+
+## Model Details
+
+| Attribute | Value |
+|:----------|:------|
+| Base Model | [WeDLM-7B](https://huggingface.co/tencent/WeDLM-7B) |
+| Parameters | 7B |
+| 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 transformers import AutoTokenizer
+from wedlm import LLM, SamplingParams
+
+llm = LLM(model="tencent/WeDLM-7B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("tencent/WeDLM-7B-Instruct", trust_remote_code=True)
+
+prompt = "Explain the difference between machine learning and deep learning."
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+outputs = llm.generate([text], SamplingParams(temperature=0.3, max_tokens=512))
+print(outputs[0]["text"])
+```
+
+### Multi-turn Conversation
+
+```python
+messages = [
+    {"role": "user", "content": "What is Python?"},
+    {"role": "assistant", "content": "Python is a high-level programming language known for its simplicity and readability."},
+    {"role": "user", "content": "Show me a hello world example."}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+outputs = llm.generate([text], SamplingParams(temperature=0.3, max_tokens=256))
+```
+
+## HuggingFace Transformers
+
+For **training** or simple forward passes:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("tencent/WeDLM-7B-Instruct", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    "tencent/WeDLM-7B-Instruct", 
+    trust_remote_code=True,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+messages = [{"role": "user", "content": "Hello!"}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, 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 | Qwen2.5-7B-Instruct | WeDLM-7B-Instruct |
+|:----------|:-------------------:|:-----------------:|
+| ARC-C (0-shot) | 86.09 | 89.59 |
+| GSM8K (3-shot) | 89.91 | 87.57 |
+| MATH (4-shot) | 45.00 | 55.40 |
+| HumanEval (4-shot) | 76.22 | 75.00 |
+| MMLU (5-shot) | 71.98 | 70.52 |
+
+## Citation (Coming soon)
+
+
+## License
+
+Apache 2.0