fix relative path for fixtures

src/axolotl/utils/models.py

@@ -129,6 +129,7 @@ def load_model(
             llm_int8_threshold=6.0,
             llm_int8_has_fp16_weight=False,
             bnb_4bit_compute_dtype=torch_dtype,
+            bnb_4bit_compute_dtype=torch_dtype,
             bnb_4bit_use_double_quant=True,
             bnb_4bit_quant_type="nf4",
         )
@@ -280,8 +281,8 @@ def load_model(
         # llama is PROBABLY model parallelizable, but the default isn't that it is
         # so let's only set it for the 4bit, see
         # https://github.com/johnsmith0031/alpaca_lora_4bit/blob/08b3fca4a4a9e0d3945be1bab4529f100a428636/finetune.py#L130-L133
-        setattr(model, 'is_parallelizable', True)
-        setattr(model, 'model_parallel', True)
+        setattr(model, "is_parallelizable", True)
+        setattr(model, "model_parallel", True)
 
     requires_grad = []
     for name, param in model.named_parameters(recurse=True):

src/axolotl/utils/trainer.py

@@ -125,7 +125,8 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
         output_dir=cfg.output_dir,
         save_total_limit=3,
         load_best_model_at_end=(
-            cfg.val_set_size > 0
+            cfg.load_best_model_at_end is not False
+            and cfg.val_set_size > 0
             and save_steps
             and save_steps % eval_steps == 0
             and cfg.load_in_8bit is not True

tests/test_prompt_tokenizers.py

@@ -1,6 +1,8 @@
+"""Module for testing prompt tokenizers."""
 import json
 import logging
 import unittest
+
 from pathlib import Path
 
 from transformers import AutoTokenizer
@@ -12,6 +14,10 @@ logging.basicConfig(level="INFO")
 
 
 class TestPromptTokenizationStrategies(unittest.TestCase):
+    """
+    Test class for prompt tokenization strategies.
+    """
+
     def setUp(self) -> None:
         self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
         self.tokenizer.add_special_tokens(
@@ -24,10 +30,15 @@ class TestPromptTokenizationStrategies(unittest.TestCase):
 
     def test_sharegpt_integration(self):
         print(Path(__file__).parent)
-        with open(Path(__file__).parent / "fixtures/conversation.json", "r") as fin:
+        with open(
+            Path(__file__).parent / "fixtures/conversation.json", encoding="utf-8"
+        ) as fin:
             data = fin.read()
             conversation = json.loads(data)
-        with open(Path(__file__).parent / "fixtures/conversation.tokenized.json", "r") as fin:
+        with open(
+            Path(__file__).parent / "fixtures/conversation.tokenized.json",
+            encoding="utf-8",
+        ) as fin:
             data = fin.read()
             tokenized_conversation = json.loads(data)
         prompter = ShareGPTPrompter("chat")