17.1K star！两小时就能训练出专属于自己的个性化小模型，这个开源项目让AI触手可及！

# 启动训练示例
python train.py \
    --model_type nano \
    --dataset ./data/corpus.txt \
    --batch_size 32 \
    --learning_rate 3e-4 \
    --max_steps 5000

class MoE(nn.Module):
    def __init__(self):
        self.experts = nn.ModuleList([TransformerBlock() for _ in range(8)])
        self.gate = nn.Linear(d_model, 8)

# 模型量化示例
python scripts/quantize_model.py \
    --input_model ./output/model_final \
    --output_model ./mobile_model \
    --quant_type int8

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{"role": "user", "content": "北京有哪些必去景点？"}],
    "temperature": 0.7
}'

from minimind import ChatAgent

agent = ChatAgent("minimind-26m-chat")
response = agent.chat("我的订单1234物流到哪里了？")
print(response)  # 您的订单正在广州转运中心...

summarizer = load_pipeline("text-summarization", model="minimind-104m")
long_text = open("report.txt").read()
summary = summarizer(long_text, max_length=100)

python scripts/preprocess.py \
    --input_dir ./raw_data \
    --output_dir ./processed \
    --lang zh \
    --min_length 50

torchrun --nproc_per_node=2 train.py \
    --model_type medium \
    --use_moe \
    --num_experts 8 \
    --save_interval 1000

from scripts.convert_model import convert_to_onnx

convert_to_onnx(
    input_path="./output/model_final",
    output_path="./deploy/model.onnx"
)