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Model: expertai/LLaMAntino-3-SLIMER-IT
Source: Original Platform

training_configs.yml

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+# fine-tuning Llama-3-8b instruct version on PileNER with topNE NEs and N samples per NE (N positive + N negative)
+
+#base_model: meta-llama/Meta-Llama-3-8B-Instruct
+#base_model: meta-llama/Meta-Llama-3-8B
+base_model: swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
+prompt_template_name: llama3_italian
+# using dataset converted from MSEQA format to GenQA format (instruction, input, output) columns
+data_path: None
+val_data_path: None
+select_train_portion: -1
+val_set_size: -1  # if -1 use all validation data
+output_dir: None
+early_stopping_patience: 5
+#training hyperparams
+batch_size: 32
+micro_batch_size: 1
+num_epochs: 10
+learning_rate: 3.0e-4
+cutoff_len: 768
+
+warmup_steps: 60
+eval_steps: 20
+logging_steps: 5
+max_grad_norm: 1.0
+#lora hyperparams
+use_lora: True
+lora_alpha: 16
+lora_dropout: 0.05
+lora_r: 8
+lora_target_modules:
+- q_proj
+- v_proj
+- k_proj
+- v_proj
+#llm hyperparams
+# NTP loss only on Response
+train_on_inputs: False
+group_by_length: True
+#quant params
+load_8bit: False
+load_4bit: False
+#general param
+save_total_limit: 2
+use_flash_attention: False
+shuffle: True
+gradient_checkpointing: False