Rewrite README: full model card with proper dataset names, intended uses, eval, citations

README.md

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+---
+library_name: transformers
+license: apache-2.0
+language: [en]
+base_model: meta-llama/Llama-3.2-3B-Instruct
+pipeline_tag: text-generation
+tags:
+  - aurora
+  - alcf
+  - hpc
+  - intel-gpu
+  - oneapi
+  - sycl
+---
+
+# Llama-3.2-3B-Aurora-Chat v3
+
+LoRA fine-tune of [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) specialized for the
+[**ALCF Aurora supercomputer**](https://docs.alcf.anl.gov/aurora/) (Intel Xeon Sapphire
+Rapids + Intel GPU Max 1550 / Ponte Vecchio, oneAPI / SYCL, PBS Pro).
+
+Off-the-shelf code-LLMs hallucinate Aurora specifics — they suggest `nvcc` instead of
+`icpx -fsycl`, `srun` / `aprun` instead of `mpiexec`, NERSC's `/global/cfs` instead of
+`/lus/flare`, and CUDA device strings instead of `xpu`. This adapter teaches the base
+model the actual Aurora toolchain, file system layout, scheduler conventions, and
+recommended PyTorch/TensorFlow/SYCL idioms.
+
+## Model summary
+
+| | |
+|---|---|
+| **Base model** | [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) |
+| **Format** | Merged 16-bit — HuggingFace Transformers / vLLM / TGI |
+| **Fine-tuning** | LoRA (PEFT) — r=32, α=64, dropout 0.0, 2 epochs |
+| **Optimizer** | AdamW fused, lr 2e-4 cosine, warmup 3%, batch 1 × grad-accum 8 |
+| **Precision / seq-len** | bf16, 1,536 tokens |
+| **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 |
+| **Train loss (final)** | 0.7200 |
+| **Hardware** | Aurora node, model-parallel across 1–12 PVC tiles via HF `device_map='auto'`, IPEX + PyTorch 2.10 XPU backend |
+| **Eval (53-Q Aurora, 0–5)** | pending |
+
+## Quick start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+tok = AutoTokenizer.from_pretrained("shazzadulimun/llama32-3b-aurora-chat-v3")
+mdl = AutoModelForCausalLM.from_pretrained("shazzadulimun/llama32-3b-aurora-chat-v3", torch_dtype=torch.bfloat16, device_map="auto")
+
+msgs = [{"role": "user", "content": "How do I launch one MPI rank per Aurora GPU tile?"}]
+ids = tok(tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True), return_tensors="pt").to(mdl.device)
+print(tok.decode(mdl.generate(**ids, max_new_tokens=400, temperature=0.0)[0][ids.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+
+## Training data
+
+Distilled from `openai/gpt-oss-120b on ALCF Sophia (vLLM)` over 416 cleaned chunks of
+[`docs.alcf.anl.gov/aurora`](https://docs.alcf.anl.gov/aurora/). 4,495
+training rows + 562 validation rows in ChatML format with embedded
+chain-of-thought (`**Reasoning:**` / `**Answer:**`).
+
+**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.
+
+Full corpus + reproduction scripts:
+[**SIslamMun/Generator @ aurora-datasets-2026-04-30**](https://github.com/SIslamMun/Generator/tree/aurora-datasets-2026-04-30/datasets/aurora/iter2/data/training/A).
+
+## Evaluation
+
+Part of the v3 parameter-size sweep (1B → 120B trained on the same dataset).
+Holdout scorecard appears here once the full sweep completes.
+
+## Limitations
+
+- **Synthetic-data biases.** Teacher (`gpt-oss-120b`) can confabulate plausible-looking
+  but incorrect commands. Treat outputs as a verifiable first draft, not authoritative.
+- **Doc snapshot is fixed at 2026-04-29.** Module versions, queue names, and APIs change
+  — anything published after that date isn't reflected here.
+- **Aurora-only.** Specifics (`/lus/flare`, `xpu`, PBS queues) won't transfer to Frontier,
+  Polaris, or other systems.
+- **Use temperature ≤ 0.1** for technical answers; higher temps invite invented flag names
+  and paths.
+
+## Citation
+
+```bibtex
+@misc{aurora-llms-2026,
+  title  = { Llama-3.2-3B-Aurora-Chat v3 },
+  author = { Islam Mun, Shazzadul },
+  year   = { 2026 },
+  url    = { https://huggingface.co/shazzadulimun/llama32-3b-aurora-chat-v3 },
+  note   = { LoRA fine-tune of Llama-3.2-3B-Instruct; data distilled from gpt-oss-120b on docs.alcf.anl.gov/aurora }
+}
+```
+
+## License
+
+Apache-2.0 for the adapter weights and synthetic training data. Source corpus is public
+ALCF user documentation. Base model retains its own license — see
+[`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct).