Every Aurora-tuned LLM trained in this project, kept together for systematic benchmarking across model size (270M → 8B, with 12B / 20B / 27B / 70B / 120B slots reserved), training recipe (chat / coder / ML / ops), and training data (multi-rank vs. single-rank distillation).

For everyday use, prefer the standalone repos at shazzadulimun:

Pick	Why
`llama31-8b-aurora-chat-v3-gguf`	Best (eval 2.80/5, +59% over base). 16 GB.
`llama32-3b-aurora-chat-v3`	Mid-size for laptop GPU. 6 GB.
`gemma3-270m-aurora-ml-v3-gguf`	Smallest. 518 MB. Runs anywhere.

Layout

aurora/
├── llama31-8b-aurora-chat-v3/      ← best chat (eval 2.80/5)
├── llama31-8b-aurora-chat-v2/      ← size-sweep recipe (eval pending)
├── llama31-8b-aurora-chat-v1/      ← single-rank distillation ablation (2.45)
├── llama31-8b-aurora-coder-v3/     ← SYCL / OpenMP / oneAPI / CMake specialist
├── llama31-8b-aurora-ml-v3/        ← PyTorch-XPU / IPEX / vLLM specialist
├── llama31-8b-aurora-ops-v3/       ← PBS / mpiexec / DAOS / Lustre specialist
├── llama32-3b-aurora-chat-v3/      ← 3B chat
├── llama32-1b-aurora-chat-v3/      ← 1B chat
├── gemma3-1b-aurora-coder-v3/
├── gemma3-1b-aurora-ml-v3/
├── gemma3-270m-aurora-coder-v3/
└── gemma3-270m-aurora-ml-v3/

Each subfolder contains either a single GGUF (*.gguf) or the full Transformers shape (config.json, model.safetensors, tokenizer.json, etc.) — depending on how the model was published.

Models — index

Subfolder	Base	Format	Train loss	Holdout (53-Q, 0–5)
`llama31-8b-aurora-chat-v3/`	meta-llama/Llama-3.1-8B-Instruct	GGUF f16	0.6224	2.80
`llama31-8b-aurora-chat-v2/`	meta-llama/Llama-3.1-8B-Instruct	merged 16-bit	0.64	pending
`llama31-8b-aurora-chat-v1/`	meta-llama/Llama-3.1-8B-Instruct	GGUF f16	0.6338	2.45
`llama31-8b-aurora-coder-v3/`	meta-llama/Llama-3.1-8B-Instruct	GGUF f16	0.6851	1.97
`llama31-8b-aurora-ml-v3/`	meta-llama/Llama-3.1-8B-Instruct	GGUF f16	0.6630	2.13
`llama31-8b-aurora-ops-v3/`	meta-llama/Llama-3.1-8B-Instruct	GGUF f16	0.6523	2.31
`llama32-3b-aurora-chat-v3/`	meta-llama/Llama-3.2-3B-Instruct	merged 16-bit	0.72	pending
`llama32-1b-aurora-chat-v3/`	meta-llama/Llama-3.2-1B-Instruct	merged 16-bit	0.84	pending
`gemma3-1b-aurora-coder-v3/`	unsloth/gemma-3-1b-it	GGUF f16	1.0268	pending
`gemma3-1b-aurora-ml-v3/`	unsloth/gemma-3-1b-it	GGUF f16	0.9609	pending
`gemma3-270m-aurora-coder-v3/`	unsloth/gemma-3-270m-it	GGUF f16	1.3203	—
`gemma3-270m-aurora-ml-v3/`	unsloth/gemma-3-270m-it	GGUF f16	1.2462	—

Download

bash

# Whole catalog (~100 GB)
hf download shazzadulimun/aurora --local-dir ./aurora-catalog

# Just one model
hf download shazzadulimun/aurora --include "llama31-8b-aurora-chat-v3/*" --local-dir ./aurora-catalog

How to use a model from this catalog

There are two formats in the catalog and they load differently.

A. Merged 16-bit checkpoints (most subfolders)

Drop-in HuggingFace Transformers — no extra steps:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo, sub = "shazzadulimun/aurora", "llama32-3b-aurora-chat-v3"
tok = AutoTokenizer.from_pretrained(repo, subfolder=sub)
mdl = AutoModelForCausalLM.from_pretrained(
    repo, subfolder=sub, torch_dtype=torch.bfloat16, device_map="auto"
)

Or grab single-file GGUFs from the standalone repos (shazzadulimun/<name>-gguf) for llama.cpp / Ollama / LM Studio.

B. LoRA-only entries (70B and 120B — for now)

llama31-70b-aurora-chat-v3/ and gpt-oss-120b-aurora-chat-v3/ contain only the LoRA adapter (the mega-train job ran out of walltime before it could write the full merged checkpoint). Until the merged + GGUF versions land here, load the base model + adapter with PEFT — works on any laptop / GPU box that fits the base:

python

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

# 70B
base = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-70B-Instruct",
    torch_dtype=torch.bfloat16, device_map="auto",
)
m = PeftModel.from_pretrained(
    base, "shazzadulimun/aurora", subfolder="llama31-70b-aurora-chat-v3"
)
tok = AutoTokenizer.from_pretrained("shazzadulimun/aurora", subfolder="llama31-70b-aurora-chat-v3")

# 120B (gpt-oss MoE)
base = AutoModelForCausalLM.from_pretrained(
    "openai/gpt-oss-120b",
    torch_dtype=torch.bfloat16, device_map="auto",
)
m = PeftModel.from_pretrained(
    base, "shazzadulimun/aurora", subfolder="gpt-oss-120b-aurora-chat-v3"
)
tok = AutoTokenizer.from_pretrained("shazzadulimun/aurora", subfolder="gpt-oss-120b-aurora-chat-v3")

You can also m = m.merge_and_unload() and m.save_pretrained("./70b-merged") to get a self-contained merged copy locally.

Training data + recipe

All models are LoRA fine-tunes (r=32, α=64, lr 2e-4 cosine, bf16, 2 epochs) of their respective base, trained on synthetic ChatML data distilled from gpt-oss-120b (ALCF Sophia) over docs.alcf.anl.gov/aurora. Three dataset variants are used across the catalog (multi-rank, single-rank ablation, topic- filtered specialists). Full provenance: SIslamMun/Generator @ aurora-datasets-2026-04-30.

License

Apache-2.0 — weights, training data (gpt-oss-120b synthetic), and source corpus (public ALCF docs). Each base model retains its own license; check before redistributing merged checkpoints.