AutoLoRA — Setup Guide¶

Source: DJLougen/autolora · 1★ Category: Agent Infrastructure / Model Optimization License: MIT · Published: June 28, 2026

AutoLoRA lets Hermes fine-tune itself. It curates successful agent traces (AUC-based selection), trains a LoRA adapter on RunPod using Unsloth, and hot-swaps the improved model into the active agent — all while keeping costs capped via Stripe payment thresholds. Set your benchmark targets, and AutoLoRA iterates until the agent meets them.

How It Works¶

1. AUC TRACE CURATION
   Agent sessions scored → top-N traces extracted → formatted as training data

2. RUNPOD + UNSLOTH LORA
   Training job launched on RunPod GPU → Unsloth QLoRA fine-tune → adapter weights returned

3. STRIPE-PAID COST CONTROL
   Training costs charged to Stripe → hard cap prevents runaway spend

4. HOT-SWAP
   New LoRA adapter loaded → agent uses improved model immediately

5. BENCHMARK VERIFICATION
   User-defined benchmarks checked → if passed, keep; if not, iterate

Installation¶

npx skills add DJLougen/autolora

Prerequisites¶

Requirement	Details
RunPod account	API key with GPU access (runpod.io)
Stripe account	Payment method for training costs
Hermes Agent	v0.16.0+ with LoRA adapter support
Training data	At least 50+ successful agent sessions for meaningful fine-tuning

export RUNPOD_API_KEY="<your-key>"
export STRIPE_API_KEY="<your-stripe-key>"
export AUTOLORA_SPEND_CAP="25"  # USD max per training run

Or in ~/.hermes/config.yaml:

env:
  RUNPOD_API_KEY: "<key>"
  STRIPE_API_KEY: "<key>"
  AUTOLORA_SPEND_CAP: "25"

skills:
  autolora:
    benchmark_threshold: 0.85
    max_iterations: 3

Usage with Hermes Agent¶

"Run autolora — fine-tune on my last 100 sessions with a 0.90 benchmark target"
"Show autolora training status"
"Hot-swap to the latest autolora adapter"

Commands¶

# Start a fine-tuning run
hermes skill run autolora --sessions 100 --target 0.85

# Check training status
hermes skill run autolora --status

# List available LoRA adapters
hermes skill run autolora --list-adapters

# Hot-swap to a specific adapter
hermes skill run autolora --swap adapter-2026-06-29-v3

Cost Estimates¶

Sessions	GPU Hours	Estimated Cost
50	~0.5	~$2-4
100	~1.0	~$4-8
500	~3.0	~$12-25
1000	~5.0	~$20-40

Based on RunPod A6000 pricing. Set AUTOLORA_SPEND_CAP conservatively.

CorpusIQ Use Cases¶

Use Case	How
Domain adaptation	Fine-tune on customer-specific workflows
Style consistency	Train on preferred code patterns and documentation style
Error reduction	Curate only successful traces to reduce mistakes
Cost optimization	Better models → fewer tokens → lower API costs

Pitfalls¶

Requires 50+ high-quality sessions before first run
RunPod cold start adds 2-5 minutes to training time
LoRA adapters are model-specific — won't transfer between base models
Over-training on a narrow task can reduce general capability
Benchmark against holdout tasks before deploying to production

This guide is part of the Hermes Skills Catalog. Discovered June 29, 2026. Powered by CorpusIQ.