50f2b94d50
* add 120b and deepspeed zero3 examples * add a bit of flavor and cleanup gpt oss readme * fix: remove expert vram usage * fix: remove redundant EOS token from eot_tokens * feat: add 120B to docs --------- Co-authored-by: NanoCode012 <nano@axolotl.ai>
68 lines
1.5 KiB
YAML
68 lines
1.5 KiB
YAML
base_model: openai/gpt-oss-20b
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use_kernels: true
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model_quantization_config: Mxfp4Config
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model_quantization_config_kwargs:
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dequantize: true
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plugins:
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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experimental_skip_move_to_device: true # prevent OOM by not putting model to GPU before sharding
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datasets:
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- path: HuggingFaceH4/Multilingual-Thinking
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type: chat_template
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field_thinking: thinking
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template_thinking_key: thinking
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dataset_prepared_path: last_run_prepared
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val_set_size: 0
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output_dir: ./outputs/gpt-oss-out/
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sequence_len: 4096
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sample_packing: true
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adapter: lora
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lora_r: 8
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lora_alpha: 16
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lora_dropout: 0.0 # dropout not supported when using LoRA over expert parameters
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lora_target_linear: true
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# TODO: not supported for now, see peft#2710
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#lora_target_parameters: # target the experts in the last two layers
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# - "22._checkpoint_wrapped_module.mlp.experts.gate_up_proj"
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# - "22._checkpoint_wrapped_module.mlp.experts.down_proj"
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# - "23._checkpoint_wrapped_module.mlp.experts.gate_up_proj"
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# - "23._checkpoint_wrapped_module.mlp.experts.down_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: 8
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micro_batch_size: 1
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num_epochs: 1
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optimizer: adamw_torch_8bit
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lr_scheduler: constant_with_warmup
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learning_rate: 2e-4
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bf16: true
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tf32: true
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flash_attention: true
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attn_implementation: kernels-community/vllm-flash-attn3
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gradient_checkpointing: true
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activation_offloading: true
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logging_steps: 1
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saves_per_epoch: 1
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warmup_ratio: 0.1
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special_tokens:
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eot_tokens:
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- "<|end|>"
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