axolotl/examples/qwen3.5

Finetune Qwen3.5 with Axolotl

Qwen3.5 is a hybrid architecture model series combining Gated DeltaNet linear attention with standard Transformer attention. All Qwen3.5 models are early-fusion vision-language models: dense variants use Qwen3_5ForConditionalGeneration and MoE variants use Qwen3_5MoeForConditionalGeneration.

Getting started

1. Install Axolotl following the installation guide.

2. Install Cut Cross Entropy to reduce training VRAM usage.

3. Install FLA for sample packing support with the Gated DeltaNet linear attention layers:

uv pip uninstall causal-conv1d && uv pip install flash-linear-attention==0.4.1

FLA is required when sample_packing: true. Without it, training raises a RuntimeError on packed sequences. Vision configs use sample_packing: false so FLA is optional there.

4. Pick any config from the table below and run:

   axolotl train examples/qwen3.5/<config>.yaml
   

Available configs:

Config	Model	Type	Peak VRAM
9b-lora-vision.yaml	Qwen3.5-9B	Vision+text LoRA, single GPU	—
9b-fft-vision.yaml	Qwen3.5-9B	Vision+text FFT, single GPU	~61 GiB
27b-qlora.yaml	Qwen3.5-27B	Dense, text-only QLoRA	~47 GiB
27b-fft.yaml	Qwen3.5-27B	Dense, text-only FFT (vision frozen)	~53 GiB
27b-qlora-fsdp.yaml	Qwen3.5-27B	Dense, text-only QLoRA + FSDP2	—
35b-a3b-moe-qlora.yaml	Qwen3.5-35B-A3B	MoE, text-only QLoRA	—
35b-a3b-moe-qlora-fsdp.yaml	Qwen3.5-35B-A3B	MoE, text-only QLoRA + FSDP2	—
122b-a10b-moe-qlora.yaml	Qwen3.5-122B-A10B	MoE, text-only QLoRA	—
122b-a10b-moe-qlora-fsdp.yaml	Qwen3.5-122B-A10B	MoE, text-only QLoRA + FSDP2	—

Gated DeltaNet Linear Attention

Qwen3.5 interleaves standard attention with Gated DeltaNet linear attention layers. To apply LoRA to them, add to lora_target_modules:

lora_target_modules:
  # ... standard projections ...
  - linear_attn.in_proj_qkv
  - linear_attn.in_proj_z
  - linear_attn.out_proj

Routed Experts (MoE)

To apply LoRA to routed expert parameters, add lora_target_parameters:

lora_target_parameters:
  - mlp.experts.gate_up_proj
  - mlp.experts.down_proj
#  - mlp.gate.weight  # router

Shared Experts (MoE)

Shared experts use nn.Linear (unlike routed experts which are 3D nn.Parameter tensors), so they can be targeted via lora_target_modules. To also train shared expert projections alongside attention, uncomment gate_up_proj and down_proj in lora_target_modules:

lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
  # Add gate_up_proj and down_proj to also target shared experts (nn.Linear):
  # - gate_up_proj
  # - down_proj

Use lora_target_parameters (see Routed Experts above) to target routed experts separately.

TIPS

• For inference hyp, please see the respective model card details.
• You can run a full finetuning of smaller configs by removing adapter: qlora and load_in_4bit: true. See Multi-GPU below.
• Read more on loading your own dataset at docs.
• The dataset format follows the OpenAI Messages format as seen here.
• For multimodal finetuning, set processor_type: AutoProcessor, skip_prepare_dataset: true, and remove_unused_columns: false as shown in 9b-lora-vision.yaml.

Optimization Guides

• Optimizations Guide

Related Resources

• Qwen3.5 Blog
• Axolotl Docs
• Axolotl Website
• Axolotl GitHub
• Axolotl Discord