Update train.py

train.py

@@ -1,60 +1,60 @@
-import unsloth  # must be first
-import pandas as pd
-import torch
-from datasets import Dataset
-from transformers import TrainingArguments
-from unsloth import FastLanguageModel
-from trl import SFTTrainer  # ✅ now works because we added 'trl'
-
-
-# Load and format your dataset
-df = pd.read_csv("data.csv")
-df["text"] = df.apply(lambda row: f"### Instruction:\n{row['instruction']}\n\n### Response:\n{row['response']}\n", axis=1)
-dataset = Dataset.from_pandas(df[["text"]])
-
-# Load Unsloth model
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "unsloth/Llama-3.2-3B-Instruct",
-    max_seq_length = 2048,
-    dtype = torch.float16,
-    load_in_4bit = True,
-)
-
-# Apply LoRA without task_type
-model = FastLanguageModel.get_peft_model(
-    model,
-    r = 8,
-    lora_alpha = 32,
-    lora_dropout = 0.05,
-    bias = "none",
-)
-
-# Tokenize text
-def tokenize(example):
-    return tokenizer(example["text"], truncation=True, padding="max_length", max_length=512)
-
-tokenized_dataset = dataset.map(tokenize, batched=True)
-
-# Set up training
-training_args = TrainingArguments(
-    output_dir = "./lora-finetuned",
-    per_device_train_batch_size = 2,
-    num_train_epochs = 3,
-    learning_rate = 2e-4,
-    logging_steps = 10,
-    save_steps = 100,
-    fp16 = True,
-)
-
-# Train
-trainer = SFTTrainer(
-    model = model,
-    tokenizer = tokenizer,
-    args = training_args,
-    train_dataset = tokenized_dataset,
-)
-
-trainer.train()
-
-# Save the fine-tuned LoRA adapter
+import unsloth  # must be first
+import pandas as pd
+import torch
+from datasets import Dataset
+from transformers import TrainingArguments
+from unsloth import FastLanguageModel
+from trl import SFTTrainer  # ✅ now works because we added 'trl'
+
+
+# Load and format your dataset
+df = pd.read_csv("data.csv")
+df["text"] = df.apply(lambda row: f"### Instruction:\n{row['instruction']}\n\n### Response:\n{row['response']}\n", axis=1)
+dataset = Dataset.from_pandas(df[["text"]])
+
+# Load Unsloth model
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/SmolLM2-1.7B-Instruct",
+    max_seq_length = 2048,
+    dtype = torch.float16,
+    load_in_4bit = True,
+)
+
+# Apply LoRA without task_type
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 8,
+    lora_alpha = 32,
+    lora_dropout = 0.05,
+    bias = "none",
+)
+
+# Tokenize text
+def tokenize(example):
+    return tokenizer(example["text"], truncation=True, padding="max_length", max_length=512)
+
+tokenized_dataset = dataset.map(tokenize, batched=True)
+
+# Set up training
+training_args = TrainingArguments(
+    output_dir = "./lora-finetuned",
+    per_device_train_batch_size = 2,
+    num_train_epochs = 3,
+    learning_rate = 2e-4,
+    logging_steps = 10,
+    save_steps = 100,
+    fp16 = True,
+)
+
+# Train
+trainer = SFTTrainer(
+    model = model,
+    tokenizer = tokenizer,
+    args = training_args,
+    train_dataset = tokenized_dataset,
+)
+
+trainer.train()
+
+# Save the fine-tuned LoRA adapter
 model.save_pretrained("./lora-finetuned")