c67910fa6f
* bump hf deps * upgrade liger-kernel too * install cce from fork for transformers fix * fix reference to vocab size in gemma3 patch * use padding_idx instead of pad_token_id * remove fixed gemma3 patch * use updated cce fork * fix local mllama cce patches w docstring * add test for multipack with trainer setup and fix trainer for trainer refactor upstream * bump modal version * guard for iterable datasetS * mllama model arch layout changed in latest transformers * fix batch sampler with drop_last * fix: address upstream vlm changes for lora * fix: update references to old lora target path * fix: remove mllama fa2 patch due to upstream fix * fix: lora kernel patch path for multimodal models * fix: removed mllama from quarto * run test for came optim on 2.6.0+ * fix fsdp2 patch and remove deprecated patch * make sure to set sequence_parallel_degree for grpo * Add SP test for GRPO * add sp to grpo config for trainer * use reward_funcs as kwarg to grpo trainer * fix the comprehension for reward funcs * reward funcs already passed in as args * init sp_group right before training * fix check for adding models to SP context * make sure to pass args to super * upgrade deepspeed * use updated trl and add reasoning flags for vllm * patch the worker --------- Co-authored-by: NanoCode012 <nano@axolotl.ai>
58 lines
1.2 KiB
YAML
58 lines
1.2 KiB
YAML
base_model: mistral-community/pixtral-12b
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processor_type: AutoProcessor
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# these 3 lines are needed for now to handle vision chat templates w images
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skip_prepare_dataset: true
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remove_unused_columns: false
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sample_packing: false
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chat_template: pixtral
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datasets:
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- path: HuggingFaceH4/llava-instruct-mix-vsft
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type: chat_template
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split: train[:1%]
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field_messages: messages
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.0
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output_dir: ./outputs/out
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adapter: lora
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lora_model_dir:
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sequence_len: 8192
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pad_to_sequence_len: false
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lora_r: 32
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_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: 4
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micro_batch_size: 1
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num_epochs: 1
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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bf16: true
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fp16:
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tf32: true
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gradient_checkpointing: true
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logging_steps: 1
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flash_attention: false # PixtralVisionModel does not support Flash Attention 2.0 yet
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eager_attention:
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warmup_ratio: 0.1
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evals_per_epoch: 1
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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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pad_token: <pad>
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