102 lines
4.5 KiB
Markdown
102 lines
4.5 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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language: [en]
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base_model: meta-llama/Llama-3.2-3B-Instruct
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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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# Llama-3.2-3B-Aurora-Chat v3
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LoRA fine-tune of [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) 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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| **Base model** | [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) |
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| **Format** | Merged 16-bit — HuggingFace Transformers / vLLM / TGI |
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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-multirank`](https://github.com/SIslamMun/Generator/tree/aurora-datasets-2026-04-30/datasets/aurora/iter2/data/training/A) — 4,495 ChatML rows |
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| **Train loss (final)** | 0.7200 |
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| **Hardware** | Aurora node, model-parallel across 1–12 PVC tiles via HF `device_map='auto'`, 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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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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tok = AutoTokenizer.from_pretrained("shazzadulimun/llama32-3b-aurora-chat-v3")
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mdl = AutoModelForCausalLM.from_pretrained("shazzadulimun/llama32-3b-aurora-chat-v3", torch_dtype=torch.bfloat16, device_map="auto")
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msgs = [{"role": "user", "content": "How do I launch one MPI rank per Aurora GPU tile?"}]
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ids = tok(tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True), return_tensors="pt").to(mdl.device)
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print(tok.decode(mdl.generate(**ids, max_new_tokens=400, temperature=0.0)[0][ids.input_ids.shape[1]:], skip_special_tokens=True))
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```
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## Training data
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Distilled from `openai/gpt-oss-120b on ALCF Sophia (vLLM)` over 416 cleaned chunks of
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[`docs.alcf.anl.gov/aurora`](https://docs.alcf.anl.gov/aurora/). 4,495
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training rows + 562 validation rows in ChatML format with embedded
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chain-of-thought (`**Reasoning:**` / `**Answer:**`).
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**Broad coverage, parallel-rank distillation.** 20 worker ranks each took a *disjoint* slice (~21 chunks) of the cleaned `docs.alcf.anl.gov/aurora` corpus and asked the teacher for chain-of-thought QA pairs. Disjoint slicing maximizes phrasing diversity (each rank sees fresh context) while still covering every chunk exactly once.
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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/A).
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## Evaluation
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Part of the v3 parameter-size sweep (1B → 120B trained on the same dataset).
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Holdout scorecard appears here once the full sweep completes.
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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 = { Llama-3.2-3B-Aurora-Chat v3 },
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author = { Islam Mun, Shazzadul },
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year = { 2026 },
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url = { https://huggingface.co/shazzadulimun/llama32-3b-aurora-chat-v3 },
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note = { LoRA fine-tune of Llama-3.2-3B-Instruct; 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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[`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct).
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