---
license: mit
language:
- zh
- en
base_model:
- deepseek-ai/deepseek-llm-7b-chat
---
# Deep **Seek-Fake-News** LLM
Project Link👁️
Lab Link👁️
### 1. Introduction of Deep **Seek-Fake-News** LLM
### 2. Model Summary
`deepseekfakenews-llm-7b-chat` is a 7B parameter model initialized from `deepseek-llm-7b-chat` and fine-tuned on extra fake news instruction data.
- **Home Page:** [DeepSeekFakeNews](https://deepseek.com/)
- **Repository:** [zt-ai/DeepSeekFakeNews-LLM-7B-Chat](https://github.com/TAN-OpenLab)
- **Demo of Chatting With DeepSeekFakeNews-LLM: to comment soon!
### 3. How to Use
Here are some examples of how to use our model.
```python
import torch
from peft import PeftModel
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "zt-ai/DeepSeekFakeNews-llm-7b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
lora_model = PeftModel.from_pretrained(base_model, model_name)
messages = [
{
"role": "user",
"content":
"""假新闻的表现可以总结为以下几个方面：1. 逻辑和事实矛盾。2.断章取义和误导性信息。3.夸张标题和吸引眼球的内容。4.情绪化和极端语言。5.偏见和单一立场。请从这几个方面分析新闻的真实性（真新闻或假新闻）：
发布时间：
新闻标题：
新闻内容：
"""}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
```
User: {messages[0]['content']}
Assistant:
```
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeekFakeNews-LLM models is subject to the Model License. DeepSeekFakeNews-LLM supports commercial use.
### 5. Contact
If you have any questions, please raise an issue or contact us at [zi4zhangt@gmail.com](mailto:zi4zhangt@gmail.com).
