---
title: deepseek-math-7b-rl
canonical_url: "https://www.modelscope.cn/models/deepseek-ai/deepseek-math-7b-rl"
md_url: "https://www.modelscope.cn/models/deepseek-ai/deepseek-math-7b-rl.md"
repository: deepseek-ai/deepseek-math-7b-rl
last_updated: 2025-02-26
license: other
model_type:
- llama
architectures:
- LlamaForCausalLM
parameters: 6.9B
tensor_type:
- BF16
library_name:
- pytorch
- transformer
- safetensors
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: 36554
stars: 5
---
# deepseek-math-7b-rl
> deepseek-math-7b-rl - deepseek-ai 在 ModelScope 开源的模型。[🏠Homepage] | [🤖 Chat with DeepSeek LLM] | [Discord] | [Wechat(微信)]
deepseek-ai/deepseek-math-7b-rl 是 ModelScope 魔搭社区上的 6.9B 参数机器学习模型，采用 other 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。
- **Repository**: deepseek-ai/deepseek-math-7b-rl
- **License**: other
- **Parameters**: 6.9B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 36554
- **Stars**: 5
- **Last updated**: 2025-02-26
Source: https://www.modelscope.cn/models/deepseek-ai/deepseek-math-7b-rl
---
[🏠Homepage] | [🤖 Chat with DeepSeek LLM] | [Discord] | [Wechat(微信)]
Paper Link👁️
### 1. Introduction to DeepSeekMath
See the [Introduction](https://github.com/deepseek-ai/DeepSeek-Math) for more details.
### 2. How to Use
Here give some examples of how to use our model.
**Chat Completion**
❗❗❗ **Please use chain-of-thought prompt to test DeepSeekMath-Instruct and DeepSeekMath-RL:**
- English questions: **{question}\nPlease reason step by step, and put your final answer within \\boxed{}.**
- Chinese questions: **{question}\n请通过逐步推理来解答问题，并把最终答案放置于\\boxed{}中。**
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-math-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "what is the integral of x^2 from 0 to 2?\nPlease reason step by step, and put your final answer within \\boxed{}."}
]
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: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['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.
### 3. License
This code repository is licensed under the MIT License. The use of DeepSeekMath models is subject to the Model License. DeepSeekMath supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL) for more details.
### 4. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
![[Wechat(微信)]](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg){kind=link}