[Ministral-3-3B] Add VLM export, INT4 quantization, and evaluation pipeline

mistralai-Ministral-3-3B-Instruct-2512/LICENSE

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mistralai-Ministral-3-3B-Instruct-2512/baseline/README.md

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+# mistralai-Ministral-3-3B-Instruct-2512 - Baseline PyTorch Evaluation
+
+This folder contains the PyTorch baseline evaluation recipe for `mistralai/Ministral-3-3B-Instruct-2512-BF16` on AI2D. It evaluates the Hugging Face BF16 model without ONNX conversion or quantization.
+
+## Setup
+
+```bash
+pip install -r requirements.txt
+```
+
+## Run baseline evaluation
+
+```bash
+python mistralai-Ministral-3-3B-Instruct-2512_pytorch_with_eval.py --device cuda --num_samples 100
+```
+
+The script defaults to `mistralai/Ministral-3-3B-Instruct-2512-BF16`, because the default FP8 checkpoint requires FP8 kernels that are not available in all PyTorch environments. To evaluate a local or alternate checkpoint, pass `--pytorch_model`.
+
+```bash
+python mistralai-Ministral-3-3B-Instruct-2512_pytorch_with_eval.py --pytorch_model /path/to/checkpoint --device cuda --num_samples 100
+```
+
+## Baseline results
+
+Evaluated on AI2D with the default BF16 Hugging Face checkpoint.
+
+| Model | Device | Precision | Samples | Accuracy | Latency (s/sample) |
+|-------|--------|-----------|---------|----------|---------------------|
+| `mistralai/Ministral-3-3B-Instruct-2512-BF16` | CUDA | FP16 | 500 | 74.20% (371/500) | 0.18 |
+
+Command:
+
+```bash
+python mistralai-Ministral-3-3B-Instruct-2512_pytorch_with_eval.py --pytorch_model mistralai/Ministral-3-3B-Instruct-2512-BF16 --device cuda --num_samples 500
+```
+
+Latency is per-sample end-to-end inference time and excludes model loading.

mistralai-Ministral-3-3B-Instruct-2512/baseline/info.yaml

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+arch: mistral3
+recipes:
+  - name: mistralai-Ministral-3-3B-Instruct-2512_pytorch_with_eval
+    file: mistralai-Ministral-3-3B-Instruct-2512_pytorch_with_eval.py
+    devices:
+      - cpu
+      - gpu
+    eps: PyTorch

mistralai-Ministral-3-3B-Instruct-2512/baseline/mistralai-Ministral-3-3B-Instruct-2512_pytorch_with_eval.py

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+"""PyTorch baseline evaluation for Ministral-3-3B on AI2D."""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+
+DEFAULT_PYTORCH_MODEL = "mistralai/Ministral-3-3B-Instruct-2512-BF16"
+
+
+def _has_arg(argv: list[str], name: str) -> bool:
+    return any(arg == name or arg.startswith(f"{name}=") for arg in argv)
+
+
+def _load_builtin_eval():
+    eval_path = Path(__file__).resolve().parents[1] / "builtin" / "eval.py"
+    spec = importlib.util.spec_from_file_location("ministral_builtin_eval", eval_path)
+    if spec is None or spec.loader is None:
+        raise RuntimeError(f"Unable to load evaluator from {eval_path}")
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def main() -> None:
+    argv = sys.argv[1:]
+    if not _has_arg(argv, "--skip_onnx"):
+        argv.insert(0, "--skip_onnx")
+    if not _has_arg(argv, "--pytorch_model"):
+        argv.extend(["--pytorch_model", DEFAULT_PYTORCH_MODEL])
+
+    sys.argv = [sys.argv[0], *argv]
+    _load_builtin_eval().main()
+
+
+if __name__ == "__main__":
+    main()

mistralai-Ministral-3-3B-Instruct-2512/baseline/requirements.txt

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+accelerate
+datasets
+pillow
+sentencepiece
+torch>=2.10.0,<2.11.0
+torchvision
+transformers>=4.57.0

mistralai-Ministral-3-3B-Instruct-2512/builtin/.gitignore

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+# Generated model artifacts
+models/
+
+# Python bytecode
+__pycache__/
+*.pyc
+
+# Olive cache
+.olive-cache/