c57acef2c7
* lora target modules with regex * updates * fsdp for non moe * update wording * chore: cleanup and lint * chore: cleanup docs from merge --------- Co-authored-by: NanoCode012 <nano@axolotl.ai>
87 lines
3.9 KiB
Markdown
87 lines
3.9 KiB
Markdown
# Finetune Qwen3.5 with Axolotl
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[Qwen3.5](https://huggingface.co/collections/Qwen/qwen35) 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`.
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## Getting started
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1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
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2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
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3. Install FLA for sample packing support with the Gated DeltaNet linear attention layers:
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```bash
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pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.4.1
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```
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> 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.
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4. Pick any config from the table below and run:
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```bash
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axolotl train examples/qwen3.5/<config>.yaml
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```
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Available configs:
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| Config | Model | Type | Peak VRAM |
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|---|---|---|---|
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| `9b-lora-vision.yaml` | Qwen3.5-9B | Vision+text LoRA, single GPU | — |
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| `9b-fft-vision.yaml` | Qwen3.5-9B | Vision+text FFT, single GPU | ~61 GiB |
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| `27b-qlora.yaml` | Qwen3.5-27B | Dense, text-only QLoRA | ~47 GiB |
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| `27b-fft.yaml` | Qwen3.5-27B | Dense, text-only FFT (vision frozen) | ~53 GiB |
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| `27b-qlora-fsdp.yaml` | Qwen3.5-27B | Dense, text-only QLoRA + FSDP2 | — |
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| `35b-a3b-moe-qlora.yaml` | Qwen3.5-35B-A3B | MoE, text-only QLoRA | — |
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| `35b-a3b-moe-qlora-fsdp.yaml` | Qwen3.5-35B-A3B | MoE, text-only QLoRA + FSDP2 | — |
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| `122b-a10b-moe-qlora.yaml` | Qwen3.5-122B-A10B | MoE, text-only QLoRA | — |
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| `122b-a10b-moe-qlora-fsdp.yaml` | Qwen3.5-122B-A10B | MoE, text-only QLoRA + FSDP2 | — |
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### Gated DeltaNet Linear Attention
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Qwen3.5 interleaves standard attention with Gated DeltaNet linear attention layers. To apply LoRA to them, add to `lora_target_modules`:
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```yaml
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lora_target_modules:
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# ... standard projections ...
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- linear_attn.in_proj_qkv
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- linear_attn.in_proj_z
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- linear_attn.out_proj
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```
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### Routed Experts (MoE)
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To apply LoRA to routed expert parameters, add `lora_target_parameters`:
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```yaml
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lora_target_parameters:
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- mlp.experts.gate_up_proj
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- mlp.experts.down_proj
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# - mlp.gate.weight # router
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```
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### Shared Experts (MoE)
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Routed experts and shared experts both have `gate_up_proj`/`down_proj`, so a plain module name in `lora_target_modules` would match both. Use a regex to target only attention and shared expert projections, while `lora_target_parameters` above handles routed experts separately:
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```yaml
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lora_target_modules: 'model\.(language_model\.)?layers\.[\d]+\.(mlp|self_attn)\.(shared_expert\.)?(up|down|gate|gate_up|q|k|v|o)_proj'
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```
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### TIPS
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- For inference hyp, please see the respective model card details.
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- You can run a full finetuning of smaller configs by removing `adapter: qlora` and `load_in_4bit: true`. See [Multi-GPU](#optimization-guides) below.
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- Read more on loading your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
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- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
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- For **multimodal** finetuning, set `processor_type: AutoProcessor`, `skip_prepare_dataset: true`, and `remove_unused_columns: false` as shown in `9b-lora-vision.yaml`.
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## Optimization Guides
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- [Optimizations Guide](https://docs.axolotl.ai/docs/optimizations.html)
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## Related Resources
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- [Qwen3.5 Blog](https://qwenlm.github.io/blog/qwen3.5/)
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- [Axolotl Docs](https://docs.axolotl.ai)
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- [Axolotl Website](https://axolotl.ai)
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- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
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- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)
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