from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "FFZwai/leyoai-analytics-small")

# Generate response
input_text = "Your question here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Base Model: Qwen/Qwen2.5-1.5B-Instruct
LoRA Rank: 16
Training Data: 27,741 samples
Validation Data: 5,945 samples
Framework: QLoRA + SFTTrainer

中文

模型描述

标准版数据分析和 SQL 生成助手

安装方法

pip install transformers peft torch

使用场景

自然语言转 SQL
数据分析
报告生成

使用方法

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# 加载基座模型
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")

# 加载 LoRA 适配器
model = PeftModel.from_pretrained(base_model, "FFZwai/leyoai-analytics-small")

# 生成回复
input_text = "你的问题"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

训练细节

基座模型: Qwen/Qwen2.5-1.5B-Instruct
LoRA Rank: 16
训练数据: 27,741 samples
验证数据: 5,945 samples
训练框架: QLoRA + SFTTrainer

About LeyoAI

LeyoAI is an AI MaaS platform by 杭州市上城区乐友信息服务工作室, providing specialized AI assistants for cybersecurity, video safety, workflow automation, and data analytics.

Website: https://leyoai.vercel.app