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Model: shazzadulimun/gemma3-270m-aurora-ml-v3-gguf Source: Original Platform
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---
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library_name: gguf
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license: apache-2.0
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language: [en]
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base_model: unsloth/gemma-3-270m-it
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pipeline_tag: text-generation
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tags:
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- aurora
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- alcf
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- hpc
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- intel-gpu
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- oneapi
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- sycl
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---
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# Gemma-3-270M-Aurora-ML v3
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LoRA fine-tune of [`unsloth/gemma-3-270m-it`](https://huggingface.co/unsloth/gemma-3-270m-it) specialized for the
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[**ALCF Aurora supercomputer**](https://docs.alcf.anl.gov/aurora/) (Intel Xeon Sapphire
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Rapids + Intel GPU Max 1550 / Ponte Vecchio, oneAPI / SYCL, PBS Pro).
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Off-the-shelf code-LLMs hallucinate Aurora specifics — they suggest `nvcc` instead of
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`icpx -fsycl`, `srun` / `aprun` instead of `mpiexec`, NERSC's `/global/cfs` instead of
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`/lus/flare`, and CUDA device strings instead of `xpu`. This adapter teaches the base
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model the actual Aurora toolchain, file system layout, scheduler conventions, and
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recommended PyTorch/TensorFlow/SYCL idioms.
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## Model summary
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| | |
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|---|---|
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| **Base model** | [`unsloth/gemma-3-270m-it`](https://huggingface.co/unsloth/gemma-3-270m-it) |
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| **Format** | GGUF, f16 — single file, llama.cpp / Ollama / LM Studio compatible |
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| **Fine-tuning** | LoRA (PEFT) — r=32, α=64, dropout 0.0, 2 epochs |
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| **Optimizer** | AdamW fused, lr 2e-4 cosine, warmup 3%, batch 1 × grad-accum 8 |
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| **Precision / seq-len** | bf16, 1,536 tokens |
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| **Training data** | [`aurora-docs-distill-v2-datascience`](https://github.com/SIslamMun/Generator/tree/aurora-datasets-2026-04-30/datasets/aurora/iter2/data/training/C2) — 1,117 ChatML rows |
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| **Train loss (final)** | 1.2462 |
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| **Hardware** | 1 Aurora PVC tile (1/12 of a node, 64 GB HBM), IPEX + PyTorch 2.10 XPU backend |
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| **Eval (53-Q Aurora, 0–5)** | pending |
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## Quick start
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**On Aurora** (PVC GPU, SYCL llama.cpp build) — interactive PBS session:
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```bash
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# 1. Grab a debug node
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qsub -I -A <project> -q debug -l select=1,walltime=01:00:00,filesystems=home:flare
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# 2. Load the toolchain
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module load frameworks
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source /lus/flare/projects/<project>/scripts/env.sh # or your own oneAPI setup
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export ONEAPI_DEVICE_SELECTOR=level_zero:gpu
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# 3. Download to flare (NOT $HOME — quota is small)
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hf download shazzadulimun/gemma3-270m-aurora-ml-v3-gguf --local-dir /lus/flare/projects/<project>/models/aurora-chat-v3
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# 4. Run on a single PVC tile
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/path/to/llama.cpp/build_sycl/bin/llama-cli \
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-m /lus/flare/projects/<project>/models/aurora-chat-v3/*.gguf \
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-ngl 999 -sm none --temp 0.0 -cnv \
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-p "How do I launch one MPI rank per GPU tile on Aurora?"
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```
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**Anywhere else** (laptop, workstation, any GPU):
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```bash
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hf download shazzadulimun/gemma3-270m-aurora-ml-v3-gguf --local-dir ./model
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./llama-cli -m ./model/*.gguf -ngl 999 --temp 0.0 -cnv
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```
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Or **Ollama / LM Studio**: `ollama run hf.co/shazzadulimun/gemma3-270m-aurora-ml-v3-gguf`
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## Training data
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Distilled from `openai/gpt-oss-120b on ALCF Sophia (vLLM)` over 130 cleaned chunks of
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[`docs.alcf.anl.gov/aurora`](https://docs.alcf.anl.gov/aurora/). 1,117
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training rows + 139 validation rows in ChatML format with embedded
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chain-of-thought (`**Reasoning:**` / `**Answer:**`).
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**Topic specialist — Data Science / AI.** Subset filtered to PyTorch/XPU, TensorFlow, vLLM, DeepSpeed, Megatron, JAX, Jupyter, and ML framework setup on Aurora. Trains a model to know `torch.xpu`, `module load frameworks`, IPEX patterns, etc.
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Full corpus + reproduction scripts:
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[**SIslamMun/Generator @ aurora-datasets-2026-04-30**](https://github.com/SIslamMun/Generator/tree/aurora-datasets-2026-04-30/datasets/aurora/iter2/data/training/C2).
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## Limitations
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- **Synthetic-data biases.** Teacher (`gpt-oss-120b`) can confabulate plausible-looking
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but incorrect commands. Treat outputs as a verifiable first draft, not authoritative.
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- **Doc snapshot is fixed at 2026-04-29.** Module versions, queue names, and APIs change
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— anything published after that date isn't reflected here.
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- **Aurora-only.** Specifics (`/lus/flare`, `xpu`, PBS queues) won't transfer to Frontier,
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Polaris, or other systems.
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- **Use temperature ≤ 0.1** for technical answers; higher temps invite invented flag names
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and paths.
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## Citation
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```bibtex
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@misc{aurora-llms-2026,
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title = { Gemma-3-270M-Aurora-ML v3 },
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author = { Islam Mun, Shazzadul },
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year = { 2026 },
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url = { https://huggingface.co/shazzadulimun/gemma3-270m-aurora-ml-v3-gguf },
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note = { LoRA fine-tune of gemma-3-270m-it; data distilled from gpt-oss-120b on docs.alcf.anl.gov/aurora }
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}
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```
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## License
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Apache-2.0 for the adapter weights and synthetic training data. Source corpus is public
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ALCF user documentation. Base model retains its own license — see
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[`unsloth/gemma-3-270m-it`](https://huggingface.co/unsloth/gemma-3-270m-it).
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gemma270m_C2-f16.gguf
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version https://git-lfs.github.com/spec/v1
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size 542834976
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