09959fac70
* feat: update mistral common * feat: add mistral3processor * fix: loading * fix: cast pixel_values to fp32 * fix: image tensor conversion * feat: add FA2 support for pixtral based models * fix: update mistral small 3.1 to use native tokenizer * fix: install tips * fix: improve info on sample dataset files * chore: move mistral configs into subfolders * fix: remove unneeded patch * fix: indent * feat: add integration tests * chore: move * feat: add magistral 2509 docs and example * fix: convert tensor to bool * feat: expand tests * chore: move tests
58 lines
1.3 KiB
YAML
58 lines
1.3 KiB
YAML
base_model: mistral-community/Mixtral-8x22B-v0.1
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# optionally might have model_type or tokenizer_type
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model_type: AutoModelForCausalLM
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tokenizer_type: LlamaTokenizer
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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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trust_remote_code: true
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unfrozen_parameters:
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- ^lm_head.weight$
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- ^model.embed_tokens.weight$
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- model.layers.4[4-9]+.block_sparse_moe.gate
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- model.layers.4[4-9]+.block_sparse_moe.experts
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- model.layers.5[0-5]+.block_sparse_moe.gate
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- model.layers.5[0-5]+.block_sparse_moe.experts
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model_config:
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output_router_logits: true
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datasets:
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- path: tatsu-lab/alpaca
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type: alpaca
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.05
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output_dir: ./outputs/out
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sequence_len: 2048
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sample_packing: true
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gradient_accumulation_steps: 1
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micro_batch_size: 1
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num_epochs: 3
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0001
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bf16: auto
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tf32: false
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gradient_checkpointing: true
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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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save_total_limit: 1
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save_steps:
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deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json
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weight_decay: 0.0
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special_tokens:
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eos_token: "<|im_end|>"
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tokens:
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- "<|im_start|>"
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# save_first_step: true # uncomment this to validate checkpoint saving works with your config
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