c119382337
* add: qwen 3.5 * test for qwen , patch * lint * qwen3 fix on main * Apply suggestions from code review Co-authored-by: NanoCode012 <kevinvong@rocketmail.com> * moe config * config moe * configs and chore * Update examples/qwen3.5/122b-a10b-moe-qlora.yaml Co-authored-by: NanoCode012 <kevinvong@rocketmail.com> * Update examples/qwen3.5/35b-a3b-moe-qlora.yaml Co-authored-by: NanoCode012 <kevinvong@rocketmail.com> * chore for qwen + vlm patch * chore lint * qwen lint * 3_5_moe * Update examples/qwen3.5/README.md --------- Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
73 lines
1.6 KiB
YAML
73 lines
1.6 KiB
YAML
base_model: Qwen/Qwen3.5-27B
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# Automatically upload checkpoint and final model to HF
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# hub_model_id: username/custom_model_name
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# Note: Qwen3.5 is an early-fusion VLM (image+text). This config fine-tunes
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# the text-only path. For multimodal (image+text) fine-tuning, add image
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# columns to your dataset following axolotl's multimodal dataset format.
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plugins:
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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strict: false
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chat_template: qwen3_5
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datasets:
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- path: mlabonne/FineTome-100k
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type: chat_template
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split: train[:20%]
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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val_set_size: 0.0
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output_dir: ./outputs/out
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dataset_prepared_path: last_run_prepared
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sequence_len: 2048
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sample_packing: true
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load_in_4bit: true
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adapter: qlora
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lora_r: 16
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lora_alpha: 32
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lora_target_modules:
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- q_proj
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- k_proj
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- v_proj
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- o_proj
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- down_proj
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- up_proj
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# Uncomment below to also target the linear attention projections.
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# These use separate in_proj_qkv / in_proj_z / out_proj (Qwen3.5-specific).
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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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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 2
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micro_batch_size: 1
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num_epochs: 1
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optimizer: adamw_torch_4bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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bf16: auto
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tf32: true
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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resume_from_checkpoint:
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logging_steps: 1
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flash_attention: true
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warmup_ratio: 0.1
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evals_per_epoch: 4
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saves_per_epoch: 1
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weight_decay: 0.0
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special_tokens:
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