johnoconnor0/lora-llama8b-aurora-baseline-2026-05-02

AURORA-Workflow-1 LoRA baseline — 2026-05-02

LoRA adapter for meta-llama/Meta-Llama-3-8B-Instruct, fit on the AURORA-Workflow-1 enriched SFT corpus on 2026-05-02. This is the H1 baseline for AURORA Stage-1 — the LoRA-tuned 8B-class transformer leg that the AURORA-M0 leg is compared against under the H1 decision rule.

H1 (verbatim). On structured workflow-apprenticeship tasks, an event-first AURORA-M0 model will achieve task success within 5 percentage points of a LoRA-tuned 8B-class transformer baseline while using at least 40 % less measured energy per successful task.

This adapter is the LoRA-tuned-8B side of that comparison.

Repository contents

Path	Role
adapter_config.json	PEFT adapter config
adapter_model.safetensors	LoRA weights (rank 16, q_proj + v_proj)
tokenizer.json, tokenizer_config.json, chat_template.jinja	Llama-3 tokeniser + chat template
ggml-adapter-model.gguf	GGML conversion for llama.cpp inference
carbontracker.json	Carbontracker capture metadata for the fit run
trainer/checkpoint-688/	Final HuggingFace Trainer checkpoint (optimizer state, RNG, args) for byte-exact reproduction

Intermediate trainer checkpoints (100, 200, …, 600) are not published — only the final checkpoint-688. Re-running scripts/fit-lora-llama.py against the same enriched corpus + the same seed reproduces every intermediate checkpoint locally.

Configuration (per ADR-0033)

Field	Value
Base model	meta-llama/Meta-Llama-3-8B-Instruct
Acceptable substitute	meta-llama/Llama-3.1-8B-Instruct (same architecture)
Target modules	q_proj, v_proj
Rank r	16
lora_alpha	32
lora_dropout	0.05
bias	none
task_type	CAUSAL_LM
Max prompt at fit time	512 tokens
Decode (inference)	greedy (temperature = 0.0, top_p = 1.0) per ADR-0022

Quick start (PEFT)

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Anthril/lora-llama8b-aurora-baseline-2026-05-02")
model = PeftModel.from_pretrained(base, "Anthril/lora-llama8b-aurora-baseline-2026-05-02")

Quick start (llama.cpp)

./llama-cli \
    -m Meta-Llama-3-8B-Instruct-Q4_K_M.gguf \
    --lora ggml-adapter-model.gguf \
    -p "<your prompt>"

Training data

The fit consumed the AURORA-Workflow-1 enriched SFT corpus generated by scripts/generate-enriched-corpus.py. Grammar source: Anthril/aurora-workflow-1.

Fit provenance (from on-disk manifests)

Field	Value
Date	2026-05-02
Git HEAD at fit	6df834a6ab669f24f9c1f4094dcf131614edaf3b
Fit script	scripts/fit-lora-llama.py
Spec anchor	ADR-0033 — LoRA-Llama baseline schema commitment
Carbontracker availability	false (not captured during this run; energy reporting is via the AURORA EnergyMeter SOP at evaluation time, not fit time)
Adapter SHA-256 (local manifest)	038542cbc70f7ceff2444cc5c243417e1418a5512c9991322056a269fe89f5f0
GGUF SHA-256	67667d43b14883b26cb4bdf53036976e539b45ad96a051fc285bac8e914d77e7
llama.cpp commit at conversion	fc2b0053ffe878ff5a26934bdb555681f15bc699

Evaluation

This adapter is consumed by aurora/evaluation_centers/runners/lora_llama_runner.py during H1 evaluation. Per ADR-0035, the comparator requires notes["energy_source"] >= 1.0 on every per-episode result — i.e. measured energy from a recognised on-die collector. Hosts without one will see the comparator emit INCONCLUSIVE_SYNTHETIC_ENERGY.

Limitations and intended use

• Research only. This adapter is published for AURORA Stage-1 H1 evaluation runs. It is not optimised for general-purpose chat or instruction following.
• Llama-3 license. Use of the underlying base model is governed by Meta's Llama 3 Community License. The LoRA adapter weights here are AURORA-original but require the gated base model to run.