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---
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 |