add AWQ quantization script (llm-compressor)

quantize_awq.py

@@ -0,0 +1,49 @@
+"""AWQ quantization via llm-compressor.
+
+Run in .venv-quant (llmcompressor + transformers 4.57.6).
+Uses domain calibration data from substrate_v5.jsonl.
+"""
+
+import json
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor import oneshot
+
+MODEL = "/workspace/substrate-qwen36-27b-merged"
+OUTPUT = "/workspace/substrate-qwen36-27b-awq"
+CALIB_DATA = "/workspace/substrate_v5.jsonl"
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+
+print("Loading model...")
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL, torch_dtype=torch.float16, device_map="auto"
+)
+
+# 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]
+
+print(f"Calibration samples: {len(calib)}")
+
+recipe = QuantizationModifier(
+    targets="Linear",
+    scheme="W4A16",
+    ignore=["lm_head"],
+)
+
+print("Running AWQ quantization...")
+oneshot(
+    model=model,
+    tokenizer=tokenizer,
+    dataset=calib,
+    recipe=recipe,
+    output_dir=OUTPUT,
+    max_seq_length=2048,
+    num_calibration_samples=len(calib),
+)
+
+print(f"Done! Output: {OUTPUT}")