Qwen3-4B-mixed-quant-RTN-wNaM

""" Mixed-quantization checkpoint: one W4A4 layer + W5-W8 for the rest.

Tests the WNA4Int code path (INT4 activation quant) alongside WNA16 and WNA8Int, without destroying model quality by using low-bit weights everywhere.

Layer 0 gets W4A4; the remaining layers cycle through: W5A16, W6A16, W7A16, W8A16, W5A8, W6A8, W7A8

Usage: python mixed_quant_w4a4.py python mixed_quant_w4a4.py --model_id Qwen/Qwen3-4B """

import argparse import os

from compressed_tensors.offload import dispatch_model from compressed_tensors.quantization import ( QuantizationArgs, QuantizationScheme, QuantizationStrategy, QuantizationType, ) from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

from llmcompressor import oneshot from llmcompressor.modifiers.quantization import QuantizationModifier

parser = argparse.ArgumentParser() parser.add_argument( "--model_id", type=str, default="Qwen/Qwen3-4B", ) args = parser.parse_args()

SAVE_DIR = ( args.model_id.rstrip("/").split("/")[-1] + "-mixed-quant-RTN" )

if os.path.exists(SAVE_DIR): print(f"Output already exists at {SAVE_DIR!r}, skipping.") exit(0)

REMAINING_FORMATS = [ # (label, weight_bits, act_bits_or_None) ("W4A16", 4, None), ("W5A16", 5, None), ("W6A16", 6, None), ("W7A16", 7, None), ("W8A16", 8, None), ("W4A8", 4, 8), ("W5A8", 5, 8), ("W6A8", 6, 8), ("W7A8", 7, 8), ]

num_layers = AutoConfig.from_pretrained(args.model_id).num_hidden_layers

config_groups = {} for i in range(num_layers): label, wbits, abits = REMAINING_FORMATS[(i - 1) % len(REMAINING_FORMATS)]

weights = QuantizationArgs(
    num_bits=wbits,
    type=QuantizationType.INT,
    strategy=QuantizationStrategy.CHANNEL,
    symmetric=True,
)

input_activations = None
if abits is not None:
    input_activations = QuantizationArgs(
        num_bits=abits,
        type=QuantizationType.INT,
        strategy=QuantizationStrategy.TOKEN,
        dynamic=True,
        symmetric=True,
    )

config_groups[f"layer_{i}_{label}"] = QuantizationScheme(
    targets=[f"re:model\\.layers\\.{i}\\..*_proj$"],
    weights=weights,
    input_activations=input_activations,
)

print(f"  layer {i:2d} -> {label}")

recipe = QuantizationModifier( config_groups=config_groups, ignore=["lm_head"], )

model = AutoModelForCausalLM.from_pretrained(args.model_id, dtype="auto") tokenizer = AutoTokenizer.from_pretrained(args.model_id)

oneshot(model=model, recipe=recipe)

print("\n\n========== SAMPLE GENERATION ==============") dispatch_model(model) input_ids = tokenizer( "Hello my name is", return_tensors="pt" ).input_ids.to(model.device) output = model.generate(input_ids, max_new_tokens=100) print(tokenizer.decode(output[0])) print("==========================================\n\n")

model.save_pretrained( SAVE_DIR, save_compressed=True, quantization_format="pack-quantized", ) tokenizer.save_pretrained(SAVE_DIR) print(f"Saved to {SAVE_DIR}")