Qwen3.5-9B-FP8-Dynamic

Qwen3.5-9B-FP8-Dynamic

FP8 dynamically quantised version of Qwen/Qwen3.5-9B, created using llm-compressor.

Quantisation Details

• Scheme: FP8_DYNAMIC (W8A8 — FP8 per-channel weights, dynamic per-token activation quantisation)
• Target layers: All Linear modules except those listed below
• Ignored layers:
  • lm_head — output projection to vocabulary
  • re:.*visual.* — entire vision encoder (patch embed, attention, MLP, merger)
  • re:.*linear_attn.* — GatedDeltaNet hybrid linear attention layers (Qwen3.5-specific architecture)

These layers remain in BF16 as they are sensitive to quantisation. In particular, the vision encoder's merger layers are a bottleneck between the visual and language representations, and the GatedDeltaNet layers contain small 32-dimensional projections that lose significant precision under FP8.

Usage with vLLM

from vllm import LLM

model = LLM("depop-ml/Qwen3.5-9B-FP8-Dynamic")

vLLM auto-detects the quantisation config from the checkpoint — no --quantization flag needed.

Usage with Transformers

from transformers import AutoModelForImageTextToText, AutoProcessor

model = AutoModelForImageTextToText.from_pretrained("depop-ml/Qwen3.5-9B-FP8-Dynamic")
processor = AutoProcessor.from_pretrained("depop-ml/Qwen3.5-9B-FP8-Dynamic")

Quantisation Recipe

from transformers import AutoModelForImageTextToText, AutoProcessor
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

model = AutoModelForImageTextToText.from_pretrained(
    "Qwen/Qwen3.5-9B", dtype="auto", trust_remote_code=True
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen3.5-9B", trust_remote_code=True)

recipe = QuantizationModifier(
    targets="Linear",
    scheme="FP8_DYNAMIC",
    ignore=[
        "lm_head",
        "re:.*visual.*",
        "re:.*linear_attn.*",
    ],
)

oneshot(model=model, recipe=recipe)

model.save_pretrained("Qwen3.5-9B-FP8-Dynamic")
processor.save_pretrained("Qwen3.5-9B-FP8-Dynamic")

Notes

• Qwen3.5 uses a hybrid architecture with both standard self-attention and GatedDeltaNet linear attention layers. The linear attention layers are excluded from quantisation as their small projection dimensions (32-dim in_proj_a and in_proj_b) appear particularly sensitive to precision loss. This is recommended by llm-compressor.
• Qwen3.5 uses a 16px patch size (vs 14px in Qwen2.5), allowing ~30% more pixels per visual token at the same inference token cost.
• Tested on NVIDIA L4 (24GB) GPUs.