---
title: DeepSeek-R1-Distill-Qwen-1.5B-risk
canonical_url: "https://www.modelscope.cn/models/caicongyang/DeepSeek-R1-Distill-Qwen-1.5B-risk"
md_url: "https://www.modelscope.cn/models/caicongyang/DeepSeek-R1-Distill-Qwen-1.5B-risk.md"
repository: caicongyang/DeepSeek-R1-Distill-Qwen-1.5B-risk
chinese_name: "DeepSeek-R1-Distill web3风控模型"
last_updated: 2025-05-03
license: "Apache License 2.0"
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
parameters: 1.8B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 22
stars: 0
---

# DeepSeek-R1-Distill-Qwen-1.5B-risk

> DeepSeek-R1-Distill-Qwen-1.5B-risk - caicongyang 在 ModelScope 开源的模型。基于DeepSeek-R1-Distill-Qwen-1.5B 微调的的web3安全风控模型

caicongyang/DeepSeek-R1-Distill-Qwen-1.5B-risk 是 ModelScope 魔搭社区上的 1.8B 参数text-generation模型，采用 Apache License 2.0 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: caicongyang/DeepSeek-R1-Distill-Qwen-1.5B-risk
- **License**: Apache License 2.0
- **Tasks**: text-generation
- **Parameters**: 1.8B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 22
- **Stars**: 0
- **Last updated**: 2025-05-03

Source: https://www.modelscope.cn/models/caicongyang/DeepSeek-R1-Distill-Qwen-1.5B-risk

---

# DeepSeek-R1-Distill-Qwen-1.5B-risk

## 模型描述
这是一个基于LoRA微调并合并后的语言模型

## 使用方法

### 使用ModelScope加载模型
```python
from modelscope.models import Model
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

model_id = "caicongyang/DeepSeek-R1-Distill-Qwen-1.5B-risk"
pipe = pipeline(Tasks.text_generation, model=model_id)
result = pipe("你好，请问你是谁？")
print(result)
```

### 使用Transformers加载模型
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "caicongyang/DeepSeek-R1-Distill-Qwen-1.5B-risk"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

inputs = tokenizer("你好，请问你是谁？", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```