Bmcbob76/echo-doctrine-generator-qlora

Echo Doctrine Generator — QLoRA Adapter v1.0

A fine-tuned QLoRA adapter for Qwen/Qwen2.5-7B-Instruct trained on 374,000+ structured doctrine blocks across 210 knowledge domains.

Model Details

Property	Value
Base Model	Qwen/Qwen2.5-7B-Instruct
Method	QLoRA (4-bit quantized LoRA)
Rank (r)	64
Alpha	128
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Adapter Size	632 MB
Training Data	374,000+ doctrine blocks from 210 domains
Training Time	13.08 hours on NVIDIA L40S 48GB
Final Loss	0.517 (train) / 0.443 (eval)

What It Does

Generates structured doctrine blocks — the fundamental knowledge units of the Echo Omega Prime engine system. Each doctrine block contains:

• Topic and Keywords — Domain-specific classification
• Conclusion Template — Expert-level summary (3-5 sentences)
• Reasoning Framework — Detailed analytical chain (20-40 lines)
• Key Factors — Critical decision elements (5+)
• Primary Authority — Legal citations, regulations, standards (3-5)
• Counter-Arguments — Opposing positions (5+)
• Confidence Stratification — DEFENSIBLE / AGGRESSIVE / DISCLOSURE / HIGH_RISK
• Resolution Strategy — How to resolve the issue

Domains Covered (210 Total)

Tax Law, Oil & Gas, Corporate Law, Real Estate, Employment Law, Criminal Law, Environmental Law, Bankruptcy, Immigration, IP Law, Family Law, Contract Law, Cybersecurity, Aerospace, Automotive, Chemical Engineering, Drilling, Marine, Mining, Nuclear, Renewable Energy, Medical, Sports Science, Weather, Cryptocurrency, and 185 more.

Usage

With vLLM (Recommended — Production)

# Serve with vLLM (LoRA enabled)
python -m vllm.entrypoints.openai.api_server   --model Qwen/Qwen2.5-7B-Instruct   --enable-lora   --lora-modules doctrine-gen=Bmcbob76/echo-doctrine-generator-qlora   --max-lora-rank 64

# Call via OpenAI-compatible API
import openai
client = openai.OpenAI(base_url="http://localhost:8000/v1", api_key="na")
response = client.chat.completions.create(
    model="doctrine-gen",
    messages=[{"role": "user", "content": "Generate a doctrine block for IRC Section 1031 like-kind exchange rules."}],
    max_tokens=800,
    temperature=0.7
)

With PEFT (Local Loading)

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

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base, "Bmcbob76/echo-doctrine-generator-qlora")
tokenizer = AutoTokenizer.from_pretrained("Bmcbob76/echo-doctrine-generator-qlora")

RunPod Serverless (Our Production Setup)

curl -X POST "https://api.runpod.ai/v2/YOUR_ENDPOINT/openai/v1/chat/completions"   -H "Authorization: Bearer YOUR_API_KEY"   -d '{"model": "doctrine-gen", "messages": [{"role": "user", "content": "Generate doctrine for..."}]}'

Training Details

• Framework: Unsloth + PEFT + Transformers
• Quantization: 4-bit QLoRA (bitsandbytes)
• Optimizer: AdamW 8-bit
• Learning Rate: 2e-4 with cosine schedule
• Epochs: 3
• Batch Size: 4 (effective 16 with gradient accumulation)
• Max Sequence Length: 2048 tokens

Part of Echo Omega Prime

This adapter is part of the Echo Omega Prime autonomous AI engine system — a network of 2,600+ specialized knowledge engines spanning 210 domains with 200,000+ doctrines.

License

Apache 2.0