---
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)

```python
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`)

```bash
./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`](https://huggingface.co/datasets/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](https://llama.meta.com/llama3/license/). The LoRA adapter weights here are AURORA-original but require the gated base model to run.