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wassname
50 lines
1.5 KiB
YAML
50 lines
1.5 KiB
YAML
# Model arguments
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model_name_or_path: HuggingFaceTB/SmolLM2-135M
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model_revision: main
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torch_dtype: bfloat16
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attn_implementation: flash_attention_2
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# Data training arguments
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tokenizer_name_or_path: HuggingFaceTB/SmolLM2-135M-Instruct # Custom tokenizer with <|im_start|> and <|im_end|> tokens
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# chat_template: "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
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dataset_mixer:
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wassname/ultrachat_200k_filtered: 1.0
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dataset_splits:
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- train_sft
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- test_sft
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preprocessing_num_workers: 12
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# SFT trainer config
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bf16: true
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do_eval: true
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eval_strategy: steps
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eval_steps: 200
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gradient_accumulation_steps: 8
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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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hub_model_id: SmolLM2-135M-sft
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hub_strategy: every_save
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learning_rate: 2.0e-04
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log_level: info
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logging_steps: 5
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logging_strategy: steps
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lr_scheduler_type: cosine
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max_seq_length: 2048
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max_steps: -1
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num_train_epochs: 3
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output_dir: /workspace/checkpoints_new/SmolLM2-135M-sft
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run_name: SmolLM2-135M-sft
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overwrite_output_dir: true
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per_device_eval_batch_size: 32
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per_device_train_batch_size: 32
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push_to_hub: false
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remove_unused_columns: true
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report_to:
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- wandb
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save_strategy: "steps"
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save_steps: 1000000
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save_total_limit: 1
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seed: 42
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warmup_ratio: 0.1
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