DeepTrans-7B

DeepTrans-7B

Quickstart

• ⛷️ Huggingface Transformers:

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:

python3 -m vllm.entrypoints.openai.api_server --model [model_ckpt] --served-model-name [model_name]

Calling LLMs:

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