102 lines
3.0 KiB
Markdown
102 lines
3.0 KiB
Markdown
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---
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license: cc-by-nc-sa-4.0
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language:
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- en
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- zh
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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tags:
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- machine tranlsation
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- O1-like model
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- Chat
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pipeline_tag: text-generation
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---
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# DeepTrans-7B
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## Quickstart
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- ⛷️ Huggingface Transformers:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Krystalan/DeepTrans-7B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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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."
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=2048
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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- ⛷️ vllm:
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Deploying LLMs:
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```bash
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python3 -m vllm.entrypoints.openai.api_server --model [model_ckpt] --served-model-name [model_name]
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```
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Calling LLMs:
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```python
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from openai import OpenAI
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# Set OpenAI's API key and API base to use vLLM's API server.
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openai_api_key = "EMPTY"
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openai_api_base = "http://localhost:8000/v1"
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client = OpenAI(
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api_key=openai_api_key,
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base_url=openai_api_base,
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)
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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."
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chat_response = client.chat.completions.create(
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model=[model_name],
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messages=[
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{"role": "user", "content": prompt},
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],
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temperature=0.1,
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top_p=0.8,
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max_tokens=2048,
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extra_body={
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"repetition_penalty": 1.05,
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},
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)
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print("Chat response:", chat_response)
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```
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## License
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This work is licensed under cc-by-nc-sa-4.0
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