---
title: DeepTrans-7B
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/DeepTrans-7B"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/DeepTrans-7B.md"
repository: AI-ModelScope/DeepTrans-7B
last_updated: 2025-06-10
license: cc-by-nc-sa-4.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
base_model:
  - Qwen/Qwen2.5-7B-Instruct
base_model_relation: finetune
parameters: 7.6B
tensor_type:
  - BF16
library_name:
  - transformer
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
  - zh
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 439
stars: 1
tags:
  - "machine tranlsation"
  - "O1-like model"
  - Chat
---

# DeepTrans-7B

> DeepTrans-7B - AI-ModelScope 在 ModelScope 开源的模型。Quickstart ⛷️ Huggingface Transformers:

AI-ModelScope/DeepTrans-7B 是 ModelScope 魔搭社区上的 7.6B 参数text-generation模型，采用 cc-by-nc-sa-4.0 许可，基于 Qwen/Qwen2.5-7B-Instruct 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: AI-ModelScope/DeepTrans-7B
- **License**: cc-by-nc-sa-4.0
- **Tasks**: text-generation
- **Parameters**: 7.6B
- **Base model**: Qwen/Qwen2.5-7B-Instruct
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: machine tranlsation, O1-like model, Chat
- **Downloads**: 439
- **Stars**: 1
- **Last updated**: 2025-06-10

Source: https://www.modelscope.cn/models/AI-ModelScope/DeepTrans-7B

---

# DeepTrans-7B


## Quickstart
- ⛷️ Huggingface Transformers:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Krystalan/DeepTrans-7B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "你是一个翻译专家，擅长将英文翻译成中文。你在翻译过程中非常擅长思考，会先进行思考再给出翻译结果。你的输出格式为：\n<think>\n[思考过程]\n</think>[翻译结果]\n\n在你思考完之后，也就是</think>之后，你会给出最终的翻译即“[翻译结果]”，且[翻译结果]中不需要给出任何解释和描述，只需要提供英文的翻译结果。\n现在请你翻译以下这句英语：\n" + "The mother, with her feet propped up on a stool, seemed to be trying to get to the bottom of that answer, whose feminine profundity had struck her all of a heap."

messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=2048
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

- ⛷️ vllm:

Deploying LLMs:
```bash
python3 -m vllm.entrypoints.openai.api_server --model [model_ckpt] --served-model-name [model_name]
```

Calling LLMs:
```python
from openai import OpenAI
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

prompt = "你是一个翻译专家，擅长将英文翻译成中文。你在翻译过程中非常擅长思考，会先进行思考再给出翻译结果。你的输出格式为：\n<think>\n[思考过程]\n</think>[翻译结果]\n\n在你思考完之后，也就是</think>之后，你会给出最终的翻译即“[翻译结果]”，且[翻译结果]中不需要给出任何解释和描述，只需要提供英文的翻译结果。\n现在请你翻译以下这句英语：\n" + "The mother, with her feet propped up on a stool, seemed to be trying to get to the bottom of that answer, whose feminine profundity had struck her all of a heap."

chat_response = client.chat.completions.create(
    model=[model_name],
    messages=[
        {"role": "user", "content": prompt},
    ],
    temperature=0.1,
    top_p=0.8,
    max_tokens=2048,
    extra_body={
        "repetition_penalty": 1.05,
    },
)
print("Chat response:", chat_response)
```



## License
This work is licensed under cc-by-nc-sa-4.0