Update Clone_Big/PETER - PARAMETERS.txt

Clone_Big/PETER - PARAMETERS.txt

@@ -1,80 +1,80 @@
-The parameters used to train this model, are encoded as follows:
-
-G-B1,DR1,ACC1,L2.5e-05,R128,A512,E20,TDFT,BF16T,GCLT,MG0.8,GCHF,D1.4,CE0.04,W0.03,cosine,R1 - THE KING
-
-
-
-Here's the actual python command to train it, we used this in Google Colab Notebook. 
-
-<CODE>
-# Begin the fine-tuning proces
-
-%cd /content/drive/MyDrive/VibeVoice-finetuning/
-
-# Define your parameters as Python variables
-batch_size = 1
-drop_rate = 0.2
-grad_accum = 1
-lr = 2.5e-5
-lora_r = 128
-lora_alpha = 512
-epochs = 20
-train_diff = True
-bf16 = True
-grad_clip = True
-max_grad = 0.8
-grad_checkpoint = False
-diff_weight = 1.4
-ce_weight = 0.04
-warmup = 0.03
-scheduler = "cosine"
-run_num = 2
-
-# Build the output directory dynamically
-output_dir =  (
-
-    f"Precise/G-B{batch_size},DR{drop_rate},ACC{grad_accum},"
-    f"L{lr},R{lora_r},A{lora_alpha},E{epochs},"
-    f"TDF{'T' if train_diff else 'F'},BF16{'T' if bf16 else 'F'},"
-    f"GCL{'T' if grad_clip else 'F'},MG{max_grad},"
-    f"GCH{'T' if grad_checkpoint else 'F'},D{diff_weight},"
-    f"CE{ce_weight},W{warmup},{scheduler},R{run_num}"
-
-              )
-
-# Now use the variables in your command
-!python -m src.finetune_vibevoice_lora \
-    --model_name_or_path vibevoice/VibeVoice-7B \
-    --processor_name_or_path src/vibevoice/processor \
-    --text_column_name text \
-    --audio_column_name audio \
-    --output_dir {output_dir} \
-    \
-    --train_jsonl GOLD_cortana_train_data.jsonl \
-    --per_device_train_batch_size {batch_size} \
-    --voice_prompt_drop_rate {drop_rate} \
-    --gradient_accumulation_steps {grad_accum} \
-    --learning_rate {lr} \
-    --lora_r {lora_r} \
-    --lora_alpha {lora_alpha} \
-    --num_train_epochs {epochs} \
-    --train_diffusion_head {train_diff} \
-    --bf16 {bf16} \
-    --gradient_clipping \
-    --max_grad_norm {max_grad} \
-    --gradient_checkpointing {grad_checkpoint} \
-    --diffusion_loss_weight {diff_weight} \
-    --ce_loss_weight {ce_weight} \
-    --warmup_ratio {warmup} \
-    --lr_scheduler_type {scheduler} \
-    \
-    --logging_steps 10 \
-    --save_steps 1528 \
-    \
-    --report_to wandb \
-    --remove_unused_columns False \
-    --do_train \
-    --ddpm_batch_mul 4 \
-    --lora_target_modules q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
-
-</CODE>
+The parameters used to train this model, are encoded as follows:
+
+G-B1,DR0.2,ACC1,L2.5e-05,R128,A512,E20,TDFT,BF16T,GCLT,MG0.8,GCHF,D1.4,CE0.04,W0.03,cosine,R1 - THE KING
+
+
+
+Here's the actual python command to train it, we used this in Google Colab Notebook. 
+
+<CODE>
+# Begin the fine-tuning proces
+
+%cd /content/drive/MyDrive/VibeVoice-finetuning/
+
+# Define your parameters as Python variables
+batch_size = 1
+drop_rate = 0.2
+grad_accum = 1
+lr = 2.5e-5
+lora_r = 128
+lora_alpha = 512
+epochs = 20
+train_diff = True
+bf16 = True
+grad_clip = True
+max_grad = 0.8
+grad_checkpoint = False
+diff_weight = 1.4
+ce_weight = 0.04
+warmup = 0.03
+scheduler = "cosine"
+run_num = 2
+
+# Build the output directory dynamically
+output_dir =  (
+
+    f"Precise/G-B{batch_size},DR{drop_rate},ACC{grad_accum},"
+    f"L{lr},R{lora_r},A{lora_alpha},E{epochs},"
+    f"TDF{'T' if train_diff else 'F'},BF16{'T' if bf16 else 'F'},"
+    f"GCL{'T' if grad_clip else 'F'},MG{max_grad},"
+    f"GCH{'T' if grad_checkpoint else 'F'},D{diff_weight},"
+    f"CE{ce_weight},W{warmup},{scheduler},R{run_num}"
+
+              )
+
+# Now use the variables in your command
+!python -m src.finetune_vibevoice_lora \
+    --model_name_or_path vibevoice/VibeVoice-7B \
+    --processor_name_or_path src/vibevoice/processor \
+    --text_column_name text \
+    --audio_column_name audio \
+    --output_dir {output_dir} \
+    \
+    --train_jsonl GOLD_cortana_train_data.jsonl \
+    --per_device_train_batch_size {batch_size} \
+    --voice_prompt_drop_rate {drop_rate} \
+    --gradient_accumulation_steps {grad_accum} \
+    --learning_rate {lr} \
+    --lora_r {lora_r} \
+    --lora_alpha {lora_alpha} \
+    --num_train_epochs {epochs} \
+    --train_diffusion_head {train_diff} \
+    --bf16 {bf16} \
+    --gradient_clipping \
+    --max_grad_norm {max_grad} \
+    --gradient_checkpointing {grad_checkpoint} \
+    --diffusion_loss_weight {diff_weight} \
+    --ce_loss_weight {ce_weight} \
+    --warmup_ratio {warmup} \
+    --lr_scheduler_type {scheduler} \
+    \
+    --logging_steps 10 \
+    --save_steps 1528 \
+    \
+    --report_to wandb \
+    --remove_unused_columns False \
+    --do_train \
+    --ddpm_batch_mul 4 \
+    --lora_target_modules q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
+
+</CODE>