如果用deepseek 训练自己的模型-by deepsek
To use DeepSeek for training your project, follow these steps based on whether you're leveraging their API or self-hosted models:1. Choose Your DeepSeek Model
DeepSeek offers models like DeepSeek-R1, DeepSeek-MoE, or chat-optimized models. Decide if you need:
API Access: For quick integration without hosting (ideal for inference or limited fine-tuning).
Open-Source Models: For full control, fine-tuning, or customization (e.g., via Hugging Face).
2. Access the Model
Option A: Use DeepSeek API
Sign Up: Get an API key from DeepSeek’s platform.
API Documentation: Review their API docs for endpoints, parameters, and rate limits.
Example API Call (Python):
import requests
api_key = "YOUR_API_KEY"
url = "https://api.deepseek.com/v1/chat/completions"
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
data = {
"model": "deepseek-chat",
"messages": [
{"role": "user", "content": "Explain how AI works."}
]
}
response = requests.post(url, json=data, headers=headers)
print(response.json()['choices']['message']['content'])
Option B: Self-Hosted Models
Download Models:
Get open-source models from Hugging Face Hub (e.g., deepseek-ai/deepseek-r1).
Use git-lfs to clone large files.
Install Dependencies:
pip install transformers torch
3. Fine-Tune the Model (Self-Hosted)
If using open-source models, fine-tune them on your dataset:
Load the Model and Tokenizer:
python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-r1")
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-r1")
Prepare Dataset:
Format your data into prompts and completions. For chat models, structure with system, user, and assistant roles.
Training Setup:
Use Hugging Face’s Trainer:
python
from transformers import TrainingArguments, Trainer
training_args = TrainingArguments(
output_dir="./results",
per_device_train_batch_size=4,
num_train_epochs=3,
logging_dir="./logs",
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset,# Your preprocessed dataset
)
trainer.train()
4. Deploy the Model
API: Directly use the API endpoint in your application.
Self-Hosted: Deploy via cloud services (AWS, GCP) or frameworks like FastAPI:
python
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Query(BaseModel):
prompt: str
@app.post("/predict")
def predict(query: Query):
inputs = tokenizer(query.prompt, return_tensors="pt")
outputs = model.generate(**inputs)
return {"response": tokenizer.decode(outputs)}
5. Resources & Considerations
Documentation: DeepSeek Official Docs for API details.
Hugging Face Integration: Use their transformers library for model loading.
Compute Requirements: Fine-tuning large models may require GPUs (e.g., A100s).
Data Privacy: For sensitive data, prefer self-hosted models over API. 在执行调用api时候发现， 不交钱不行。。。在纠结要不要自己花钱。。。首先我就是有兴趣，但是抠门的人不想花太多钱， 不知道图书馆会不会有免费的使用 据国内的人说不贵， 但是目前官网不能直接付款
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