support for replit lm
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55
examples/replit-3b/config-lora.yml
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55
examples/replit-3b/config-lora.yml
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@@ -0,0 +1,55 @@
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base_model: replit/replit-code-v1-3b
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base_model_config: replit/replit-code-v1-3b
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trust_remote_code: true
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load_in_8bit: false
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datasets:
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- path: vicgalle/alpaca-gpt4
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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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adapter: lora
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lora_model_dir:
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sequence_len: 2048
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max_packed_sequence_len:
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lora_r: 8
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_modules:
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- Wqkv
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- mlp_up
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- mlp_down
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lora_fan_in_fan_out:
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wandb_project: lora-replit
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wandb_watch:
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wandb_run_id:
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wandb_log_model:
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output_dir: ./lora-replit
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batch_size: 8
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micro_batch_size: 1
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num_epochs: 3
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optimizer:
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torchdistx_path:
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lr_scheduler:
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learning_rate: 0.00001
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train_on_inputs: false
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group_by_length: false
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bf16: true
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tf32: true
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gradient_checkpointing:
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention:
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gptq_groupsize:
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gptq_model_v1:
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warmup_steps: 20
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eval_steps: 50
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save_steps:
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debug:
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deepspeed:
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weight_decay: 0
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fsdp:
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fsdp_config:
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#special_tokens:
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@@ -163,11 +163,20 @@ def load_model(
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if not tokenizer:
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try:
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if is_llama_derived_model and "LlamaTokenizer" in globals():
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tokenizer = LlamaTokenizer.from_pretrained(model)
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tokenizer = LlamaTokenizer.from_pretrained(
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model,
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trust_remote_code=True if cfg.trust_remote_code is True else False,
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)
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else:
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tokenizer = getattr(transformers, tokenizer_type).from_pretrained(model)
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tokenizer = getattr(transformers, tokenizer_type).from_pretrained(
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model,
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trust_remote_code=True if cfg.trust_remote_code is True else False,
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)
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except:
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tokenizer = AutoTokenizer.from_pretrained(base_model_config)
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tokenizer = AutoTokenizer.from_pretrained(
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base_model_config,
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trust_remote_code=True if cfg.trust_remote_code is True else False,
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)
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logging.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
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logging.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
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