self_gen temp fix + lv1 temp varying configs

configs/qa_short_ctx_self_gen_lv1_tiny_temp_0.3.yaml

@@ -0,0 +1,52 @@
+output_dir: "" # just a placeholder
+bf16: true
+model_name_or_path: google/gemma-3-1b-it
+label_names: ["labels"]
+# eval_on_start: True
+# eval_strategy: "steps"
+# eval_steps: 500
+# save_strategy: "no"
+# # save_steps: 500
+# logging_strategy: "steps"
+# logging_steps: 100
+# use_liger_kernel: true
+# remove_unused_columns: false
+
+# needed to avoid OOM by compute the metrics batch by batch
+# w/o this the trainer stores logits of all sample in memory...
+# batch_eval_metrics: true
+
+per_device_train_batch_size: 8
+per_device_eval_batch_size: 8
+max_val_samples_per_ds: 1000
+# optim: schedule_free_adamw
+
+learning_rate: 0.00004
+# lr_scheduler_type: "constant_with_warmup"
+neftune_noise_alpha: 1
+weight_decay: 0.01
+
+warmup_steps: 100
+
+dataloader_prefetch_factor: 16
+dataloader_num_workers: 8
+
+# LoRA
+lora_r: 8
+lora_dropout: 0.0
+target_modules:
+  - down_proj
+
+# data
+train_ds_names:
+  - self_gen/google/gemma-2-2b-it_temp_0.3/fw_qa_v2_2k_len_level_1_tiny
+  - self_gen/google/gemma-2-2b-it_temp_0.3/squad_compact
+  - self_gen/google/gemma-2-2b-it_temp_0.3/pwc_compact
+  - self_gen/google/gemma-2-2b-it_temp_0.3/ropes_compact
+  - self_gen/google/gemma-2-2b-it_temp_0.3/drop_compact
+
+val_ds_names:
+  - squad
+  - pwc
+  - drop
+  - ropes

configs/qa_short_ctx_self_gen_lv1_tiny_temp_0.5.yaml

@@ -0,0 +1,52 @@
+output_dir: "" # just a placeholder
+bf16: true
+model_name_or_path: google/gemma-3-1b-it
+label_names: ["labels"]
+# eval_on_start: True
+# eval_strategy: "steps"
+# eval_steps: 500
+# save_strategy: "no"
+# # save_steps: 500
+# logging_strategy: "steps"
+# logging_steps: 100
+# use_liger_kernel: true
+# remove_unused_columns: false
+
+# needed to avoid OOM by compute the metrics batch by batch
+# w/o this the trainer stores logits of all sample in memory...
+# batch_eval_metrics: true
+
+per_device_train_batch_size: 8
+per_device_eval_batch_size: 8
+max_val_samples_per_ds: 1000
+# optim: schedule_free_adamw
+
+learning_rate: 0.00004
+# lr_scheduler_type: "constant_with_warmup"
+neftune_noise_alpha: 1
+weight_decay: 0.01
+
+warmup_steps: 100
+
+dataloader_prefetch_factor: 16
+dataloader_num_workers: 8
+
+# LoRA
+lora_r: 8
+lora_dropout: 0.0
+target_modules:
+  - down_proj
+
+# data
+train_ds_names:
+  - self_gen/google/gemma-2-2b-it_temp_0.5/fw_qa_v2_2k_len_level_1_tiny
+  - self_gen/google/gemma-2-2b-it_temp_0.5/squad_compact
+  - self_gen/google/gemma-2-2b-it_temp_0.5/pwc_compact
+  - self_gen/google/gemma-2-2b-it_temp_0.5/ropes_compact
+  - self_gen/google/gemma-2-2b-it_temp_0.5/drop_compact
+
+val_ds_names:
+  - squad
+  - pwc
+  - drop
+  - ropes

configs/qa_short_ctx_self_gen_lv1_tiny_temp_0.7.yaml

@@ -0,0 +1,52 @@
+output_dir: "" # just a placeholder
+bf16: true
+model_name_or_path: google/gemma-3-1b-it
+label_names: ["labels"]
+# eval_on_start: True
+# eval_strategy: "steps"
+# eval_steps: 500
+# save_strategy: "no"
+# # save_steps: 500
+# logging_strategy: "steps"
+# logging_steps: 100
+# use_liger_kernel: true
+# remove_unused_columns: false
+
+# needed to avoid OOM by compute the metrics batch by batch
+# w/o this the trainer stores logits of all sample in memory...
+# batch_eval_metrics: true
+
+per_device_train_batch_size: 8
+per_device_eval_batch_size: 8
+max_val_samples_per_ds: 1000
+# optim: schedule_free_adamw
+
+learning_rate: 0.00004
+# lr_scheduler_type: "constant_with_warmup"
+neftune_noise_alpha: 1
+weight_decay: 0.01
+
+warmup_steps: 100
+
+dataloader_prefetch_factor: 16
+dataloader_num_workers: 8
+
+# LoRA
+lora_r: 8
+lora_dropout: 0.0
+target_modules:
+  - down_proj
+
+# data
+train_ds_names:
+  - self_gen/google/gemma-2-2b-it_temp_0.7/fw_qa_v2_2k_len_level_1_tiny
+  - self_gen/google/gemma-2-2b-it_temp_0.7/squad_compact
+  - self_gen/google/gemma-2-2b-it_temp_0.7/pwc_compact
+  - self_gen/google/gemma-2-2b-it_temp_0.7/ropes_compact
+  - self_gen/google/gemma-2-2b-it_temp_0.7/drop_compact
+
+val_ds_names:
+  - squad
+  - pwc
+  - drop
+  - ropes

configs/qa_short_ctx_self_gen_lv1_tiny_temp_1.0.yaml

@@ -0,0 +1,52 @@
+output_dir: "" # just a placeholder
+bf16: true
+model_name_or_path: google/gemma-3-1b-it
+label_names: ["labels"]
+# eval_on_start: True
+# eval_strategy: "steps"
+# eval_steps: 500
+# save_strategy: "no"
+# # save_steps: 500
+# logging_strategy: "steps"
+# logging_steps: 100
+# use_liger_kernel: true
+# remove_unused_columns: false
+
+# needed to avoid OOM by compute the metrics batch by batch
+# w/o this the trainer stores logits of all sample in memory...
+# batch_eval_metrics: true
+
+per_device_train_batch_size: 8
+per_device_eval_batch_size: 8
+max_val_samples_per_ds: 1000
+# optim: schedule_free_adamw
+
+learning_rate: 0.00004
+# lr_scheduler_type: "constant_with_warmup"
+neftune_noise_alpha: 1
+weight_decay: 0.01
+
+warmup_steps: 100
+
+dataloader_prefetch_factor: 16
+dataloader_num_workers: 8
+
+# LoRA
+lora_r: 8
+lora_dropout: 0.0
+target_modules:
+  - down_proj
+
+# data
+train_ds_names:
+  - self_gen/google/gemma-2-2b-it_temp_1.0/fw_qa_v2_2k_len_level_1_tiny
+  - self_gen/google/gemma-2-2b-it_temp_1.0/squad_compact
+  - self_gen/google/gemma-2-2b-it_temp_1.0/pwc_compact
+  - self_gen/google/gemma-2-2b-it_temp_1.0/ropes_compact
+  - self_gen/google/gemma-2-2b-it_temp_1.0/drop_compact
+
+val_ds_names:
+  - squad
+  - pwc
+  - drop
+  - ropes

data/self_generate_qa.py

@@ -74,8 +74,13 @@ def get_dataset_configs(
 
         # Process train datasets
         train_ds_names = config.get("train_ds_names", [])
+        # self_gen_train_ds_names = [
+        #     (ds_name.split("/")[-1], "train")
+        #     for ds_name in train_ds_names
+        #     if ds_name.startswith("self_gen/")
+        # ]
         self_gen_train_ds_names = [
-            (ds_name.split("/")[-1], "train")
+            (ds_name, "train")
             for ds_name in train_ds_names
             if ds_name.startswith("self_gen/")
         ]
@@ -140,14 +145,7 @@ def self_generate(
         temp = float(ds_name.split("_temp_")[-1].split("/")[0])
     print(f"Processing dataset: {ds_name}, split: {split}")
     print(f"Using temperature: {temp}")
-    kwargs = dict(
-        ds_name=ds_name,
-        split=split,
-        add_negative_prompt=False,
-        add_repeat_prompt=False,
-        repeat_prob=0,
-        is_pretrain=False,
-    )
+
     if parquet_file:
         print(f"Loading dataset from parquet file: {parquet_file}")
 
@@ -163,6 +161,14 @@ def self_generate(
         ds_name = ds_name.split("/")[-1]  # Extract just the dataset name
 
         print(f"Loading dataset: {ds_name} with split: {split}")
+        kwargs = dict(
+            ds_name=ds_name,
+            split=split,
+            add_negative_prompt=False,
+            add_repeat_prompt=False,
+            repeat_prob=0,
+            is_pretrain=False,
+        )
 
         ds = load_and_process_dataset(**kwargs, streaming=False, num_proc=8)