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axolotl/examples/qwen3.5/35b-a3b-moe-vision-lora.yaml
2026-04-10 16:46:17 -04:00

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YAML

# Qwen 3.5 35B-A3B MoE Vision LoRA
#
# Vision fine-tuning of the hybrid DeltaNet + Attention MoE model.
# 256 experts, 8 active per token, with early-fusion vision support.
base_model: Qwen/Qwen3.5-35B-A3B
processor_type: AutoProcessor
# Required for vision/multimodal training
skip_prepare_dataset: true
remove_unused_columns: false
sample_packing: false
chat_template: qwen3_5
datasets:
- path: HuggingFaceH4/llava-instruct-mix-vsft
type: chat_template
split: train[:100]
val_set_size: 0
output_dir: ./outputs/qwen35-35b-a3b-vision-lora
adapter: lora
sequence_len: 4096
pad_to_sequence_len: false
lora_r: 16
lora_alpha: 32
lora_dropout: 0
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- down_proj
- up_proj
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 1
max_steps: 10
optimizer: adamw_torch_8bit
lr_scheduler: cosine
learning_rate: 0.0002
bf16: auto
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
logging_steps: 1
flash_attention: true
warmup_ratio: 0.1
weight_decay: 0.0
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model: