FFZwai/leyoai-analytics-small

LeyoAI Analytics Assistant (Small)

English | 中文

English

Model Description

Standard data analytics and SQL generation assistant

Installation

pip install transformers peft torch

Usage Scenarios

• Natural language to SQL
• Data analysis
• Report generation

How to Use

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

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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

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

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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