marauder-actual
2200120133
Training examples are ~500-1500 tokens. seqlen=2048 causes 'no data has been cached' error. Also remove deprecated format param.
61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
"""INT4 quantization via AutoRound (Intel).
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Run in .venv-quant with transformers 5.x.
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llm-compressor is pinned to transformers <=4.57.6 and can't load Qwen3.6.
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AutoRound works with transformers 5.x and produces vLLM-compatible output.
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Uses domain calibration data from substrate_v5.jsonl.
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"""
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import json
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Qwen3.6-27B uses Qwen3_5ForConditionalGeneration which AutoRound
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# misidentifies as a multimodal model. Patch before importing AutoRound.
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import auto_round.autoround as _ar
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_ar.is_mllm_model = lambda *a, **kw: False
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from auto_round import AutoRound
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MODEL = "/workspace/substrate-qwen36-27b-merged"
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OUTPUT = "/workspace/substrate-qwen36-27b-int4"
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CALIB_DATA = "/workspace/substrate_v5.jsonl"
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print("Loading tokenizer...")
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tokenizer = AutoTokenizer.from_pretrained(MODEL)
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print("Loading model...")
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model = AutoModelForCausalLM.from_pretrained(
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MODEL, torch_dtype=torch.float16, device_map="auto"
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)
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# Domain calibration from training data (chat format → raw text)
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print("Loading calibration data...")
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with open(CALIB_DATA) as f:
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calib = []
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for line in f:
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msgs = json.loads(line)["messages"]
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text = tokenizer.apply_chat_template(msgs, tokenize=False)
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calib.append(text)
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print(f"Calibration samples: {len(calib)}")
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rounder = AutoRound(
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model=model,
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tokenizer=tokenizer,
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dataset=calib,
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bits=4,
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group_size=128,
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seqlen=512,
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nsamples=min(128, len(calib)),
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iters=200,
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)
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print("Running AutoRound INT4 quantization...")
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rounder.quantize()
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print(f"Saving to {OUTPUT}...")
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rounder.save_quantized(OUTPUT, format="auto_round")
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print(f"Done! Output: {OUTPUT}")
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