switch quantization from llm-compressor to AutoRound

llm-compressor pins transformers<=4.57.6, can't load Qwen3.6.
AutoRound (Intel) works with transformers 5.x and is already
installed as an llmcompressor dependency. Produces vLLM-compatible
INT4 output.

quantize_awq.py

@@ -1,25 +1,22 @@
-"""AWQ quantization via llm-compressor.
+"""INT4 quantization via AutoRound (Intel).
 
-Run in .venv-quant (llmcompressor + transformers 4.57.6).
+Run in .venv-quant with transformers 5.x.
+llm-compressor is pinned to transformers <=4.57.6 and can't load Qwen3.6.
+AutoRound works with transformers 5.x and produces vLLM-compatible output.
 Uses domain calibration data from substrate_v5.jsonl.
 """
 
 import json
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-from llmcompressor.modifiers.quantization import QuantizationModifier
+from auto_round import AutoRound
-from llmcompressor import oneshot
 
 MODEL = "/workspace/substrate-qwen36-27b-merged"
-OUTPUT = "/workspace/substrate-qwen36-27b-awq"
+OUTPUT = "/workspace/substrate-qwen36-27b-int4"
 CALIB_DATA = "/workspace/substrate_v5.jsonl"
-# Merged model was saved with transformers 5.5.0 which writes
-# tokenizer_class=TokenizersBackend — unknown to 4.57.6 in the quant venv.
-# Load tokenizer from original repo (has standard class name).
-TOKENIZER_SOURCE = "Qwen/Qwen3.6-27B"
 
 print("Loading tokenizer...")
-tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_SOURCE)
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
 
 print("Loading model...")
 model = AutoModelForCausalLM.from_pretrained(
@@ -29,25 +26,26 @@ model = AutoModelForCausalLM.from_pretrained(
 # Domain calibration from training data
 print("Loading calibration data...")
 with open(CALIB_DATA) as f:
-    calib = [json.loads(line)["text"] for line in f][:128]
+    calib = [json.loads(line)["text"] for line in f]
 
 print(f"Calibration samples: {len(calib)}")
 
-recipe = QuantizationModifier(
+rounder = AutoRound(
-    targets="Linear",
-    scheme="W4A16",
-    ignore=["lm_head"],
-)
-
-print("Running AWQ quantization...")
-oneshot(
     model=model,
     tokenizer=tokenizer,
     dataset=calib,
-    recipe=recipe,
+    bits=4,
-    output_dir=OUTPUT,
+    group_size=128,
-    max_seq_length=2048,
+    seqlen=2048,
-    num_calibration_samples=len(calib),
+    nsamples=min(128, len(calib)),
+    iters=200,
+    format="auto_round",
 )
 
+print("Running AutoRound INT4 quantization...")
+rounder.quantize()
+
+print(f"Saving to {OUTPUT}...")
+rounder.save_quantized(OUTPUT, format="auto_round")
+
 print(f"Done! Output: {OUTPUT}")