bug fixes

.gitignore

@@ -28,6 +28,8 @@ share/python-wheels/
 MANIFEST
 /models
 /unsloth_compiled_cache
+/gitclone/enterprise
+/gitclone/odoo
 
 # PyInstaller
 #  Usually these files are written by a python script from a template

configs/training_config.yaml

@@ -13,9 +13,10 @@ training:
   # Memory-optimized batch size for RTX3070 8GB
   per_device_train_batch_size: 2
   gradient_accumulation_steps: 4
+  max_steps: 30
 
   # Training parameters
-  num_train_epochs: 3
+  num_train_epochs: 1
   learning_rate: 2.0e-4
   warmup_steps: 10
   warmup_ratio: 0.1
@@ -42,7 +43,7 @@ training:
   offload_to_cpu: false  # Explicitly no CPU offloading
 
   # Optimizer settings
-  optim: "adamw_torch"
+  optim: "paged_adamw_8bit"
   weight_decay: 0.01
   adam_beta1: 0.9
   adam_beta2: 0.999
@@ -50,7 +51,7 @@ training:
   max_grad_norm: 1.0
 
   # Learning rate scheduler
-  lr_scheduler_type: "cosine"
+  lr_scheduler_type: "linear"
 
   # Precision - BF16 for better stability on modern GPUs
   bf16: true
@@ -59,7 +60,7 @@ training:
 
   # Dataset settings
   dataset_shuffle: true
-  dataset_seed: 42
+  dataset_seed: 3407
 
   # Output settings
   output_dir: "./models"

src/dataset_processor.py

@@ -179,8 +179,8 @@ class DatasetProcessor:
 
         finally:
             # Cleanup temporary directories, but keep gitclone folder
-            if temp_dir != "./gitclone":
-                shutil.rmtree(temp_dir, ignore_errors=True)
+            # if temp_dir != "./gitclone":
+            #     shutil.rmtree(temp_dir, ignore_errors=True)
 
     def _extract_code_samples(self, repo_path: str, config: AppConfig) -> List[Dict]:
         """

src/trainer.py

@@ -16,8 +16,10 @@ from transformers import (
     AutoTokenizer,
     Trainer,
     TrainingArguments,
-    DataCollatorForLanguageModeling
+    DataCollatorForLanguageModeling,
+    DataCollatorForSeq2Seq
 )
+from trl import SFTConfig, SFTTrainer
 from datasets import Dataset
 from unsloth import FastLanguageModel, is_bfloat16_supported
 
@@ -178,7 +180,7 @@ class ModelTrainer:
         self.logger.info("Setting up trainer...")
 
         # Training arguments optimized for RTX3070 8GB
-        training_args = TrainingArguments(
+        training_args = SFTConfig(
             output_dir=str(self.output_dir / "checkpoints"),
             num_train_epochs=self.config.training.num_train_epochs,
             per_device_train_batch_size=self.config.training.per_device_train_batch_size,
@@ -216,22 +218,26 @@ class ModelTrainer:
             # Memory optimization settings
             ddp_find_unused_parameters=False,
             per_device_eval_batch_size=self.config.training.per_device_train_batch_size,
+            max_steps=self.config.training.max_steps if hasattr(self.config.training, 'max_steps') else 10
         )
 
         # Data collator
-        data_collator = DataCollatorForLanguageModeling(
-            tokenizer=self.tokenizer,
-            mlm=False  # Causal language modeling
-        )
+        # data_collator = DataCollatorForLanguageModeling(
+        #     tokenizer=self.tokenizer,
+        #     mlm=False  # Causal language modeling
+        # )
+        data_collator = DataCollatorForSeq2Seq(tokenizer=self.tokenizer)
 
         # Initialize trainer
-        self.trainer = Trainer(
+        self.trainer = SFTTrainer(
             model=self.model,
             args=training_args,
             train_dataset=tokenized_dataset,
             eval_dataset=tokenized_dataset,  # Using same dataset for eval (for demo)
             data_collator=data_collator,
             tokenizer=self.tokenizer,
+            dataset_text_field="text",
+            packing=False  # Can make training 5x faster for short sequences.
         )
 
         self.logger.info("Trainer setup completed")