license: llama3.1
base_model: meta-llama/Meta-Llama-3-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- aurora
- workflow
- lora
- baseline
- llama-3
- peft
language:
- en
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.