feat(tools): Mistral Small 4 NVFP4 build pipeline (quant + HF->native converter)

Quantize a HF-format Mistral Small 4 (Mistral3ForConditionalGeneration MoE) to
NVFP4 with the vision tower intact, then convert HF NVFP4 -> Mistral native so
vLLM can serve it (there is no HF Mistral4 serving path in any vLLM version).

Built + validated end-to-end on ana-ml2 for the abliterated character-model
successor (darkc0de/Mistral-Small-4-119B-2603-heretic): quant -> dry-run (clean
vs the official native NVFP4 reference) -> convert -> serve-test (loads on the
native loader, correct text, vision functional).

Converter scaffold came from worldtree-codex (bf16 bin maps + fused-expert
split); fixed here: NVFP4 layer regexes (keep the `model.` prefix) + non-mmap
shard reads (ZFS large-mmap ENOMEM). nvfp4_quant.py is local. README documents
the pipeline + every gotcha that cost a failed run. Homed here per operator
direction (not Worldtree).

tools/mistral-small4-nvfp4/nvfp4_quant.py

@@ -0,0 +1,26 @@
+import sys, torch
+from transformers import AutoModelForImageTextToText, AutoTokenizer
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.modeling.moe.linearize import load_quantizable_moe
+
+MODEL, OUT = sys.argv[1], sys.argv[2]
+NSAMPLES = int(sys.argv[3]) if len(sys.argv) > 3 else 64
+
+# Mirror Mistral official NVFP4: quantize expert FFN (routed via MoE-linearize + shared),
+# keep bf16: vision, all MLA attention, MoE router gate, embeddings, lm_head
+IGNORE = [
+    "re:.*lm_head.*", "re:.*embed_tokens.*",
+    "re:.*vision_tower.*", "re:.*multi_modal_projector.*",
+    "re:.*self_attn.*", r"re:.*mlp\.gate$",
+]
+recipe = QuantizationModifier(targets="Linear", scheme="NVFP4", ignore=IGNORE)
+
+with load_quantizable_moe(AutoModelForImageTextToText):
+    model = AutoModelForImageTextToText.from_pretrained(MODEL, dtype="auto", device_map="cpu")
+
+oneshot(model=model, recipe=recipe, processor=AutoTokenizer.from_pretrained(MODEL), dataset="ultrachat_200k",
+        splits={"calibration": f"train_gen[:{NSAMPLES}]"},
+        num_calibration_samples=NSAMPLES, max_seq_length=512)
+model.save_pretrained(OUT, save_compressed=True)
+print("SAVED", OUT)