Bmcbob76/echo-engineering-adapter

Echo Engineering Adapter

Part of the Echo Omega Prime AI engine collection — domain-specialized LoRA adapters built on Qwen2.5-7B-Instruct.

Overview

Structural and mechanical engineering analysis covering stress analysis, material selection, fatigue life, and design optimization.

Domain: Engineering & Structural Analysis

Training Details

Parameter	Value
Base Model	Qwen/Qwen2.5-7B-Instruct
Method	QLoRA (4-bit NF4 quantization + LoRA)
LoRA Rank (r)	16
LoRA Alpha	32
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training Data	Engineering doctrine blocks covering structural analysis, fatigue, thermal, tolerance stack-up, and material properties
Epochs	3
Loss	converged
Adapter Size	~38 MB
Framework	PEFT + Transformers + bitsandbytes
Precision	bf16 (adapter) / 4-bit NF4 (base during training)

Usage with PEFT

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-7B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "Bmcbob76/echo-engineering-adapter")

# Generate
messages = [
    {"role": "system", "content": "You are a domain expert in Engineering & Structural Analysis."},
    {"role": "user", "content": "Perform a structural fatigue analysis for this drill pipe section under cyclic bending loads with corrosion factor considerations."},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.3)

print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

vLLM Multi-Adapter Serving

python -m vllm.entrypoints.openai.api_server \
    --model Qwen/Qwen2.5-7B-Instruct \
    --enable-lora \
    --lora-modules 'echo-engineering-adapter=Bmcbob76/echo-engineering-adapter'

Then query via OpenAI-compatible API:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="token")
response = client.chat.completions.create(
    model="echo-engineering-adapter",
    messages=[
        {"role": "system", "content": "You are a domain expert in Engineering & Structural Analysis."},
        {"role": "user", "content": "Perform a structural fatigue analysis for this drill pipe section under cyclic bending loads with corrosion factor considerations."},
    ],
    temperature=0.3,
    max_tokens=1024,
)
print(response.choices[0].message.content)

Echo Omega Prime Collection

This adapter is part of the Echo Omega Prime intelligence engine system — 2,600+ domain-specialized engines spanning law, engineering, medicine, cybersecurity, oil & gas, and more.

Adapter	Domain
echo-titlehound-lora	Oil & Gas Title Examination
echo-doctrine-generator-qlora	AI Doctrine Generation
echo-landman-adapter	Landman Operations
echo-taxlaw-adapter	Tax Law & IRC
echo-legal-adapter	Legal Analysis
echo-realestate-adapter	Real Estate Law
echo-cyber-adapter	Cybersecurity
echo-engineering-adapter	Engineering Analysis
echo-medical-adapter	Medical & Clinical
echo-software-adapter	Software & DevOps

License

Apache 2.0